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1
Chu, Kai, and Guang-Hai Liu. "Image Retrieval Based on a Multi-Integration Features Model." Mathematical Problems in Engineering 2020 (March 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/1461459.
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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.
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Mahajan, Vipul R., and Alka Khade. "A Survey: Content Based Image Retrieval using Block Truncation Coding." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 46. http://dx.doi.org/10.23956/ijarcsse.v7i12.495.
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A new approach to index color images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two color quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, colour histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calculated from CHF and BHF can be utilized to measure the similarity between two images. A new approach to index colour images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two colour quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, color histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calculated from CHF and BHF can be utilized to measure the similarity between two images.
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Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-311-2016.
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Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.
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Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprs-archives-xli-b1-311-2016.
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Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.
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Martey, Ezekiel Mensah, Hang Lei, Xiaoyu Li, and Obed Appiah. "Image Representation Using Stacked Colour Histogram." Algorithms 14, no. 8 (2021): 228. http://dx.doi.org/10.3390/a14080228.
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Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.
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Essa, Almabrok, and Vijayan K. Asari. "Histogram of Oriented Directional Features for Robust Face Recognition." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3 (2016): 35–51. http://dx.doi.org/10.4018/ijmstr.2016070103.
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This paper presents an illumination invariant face recognition system that uses directional features and modular histogram. The proposed Histogram of Oriented Directional Features (HODF) produces multi-region histograms for each face image, then concatenates these histograms to form the final feature vector. This feature vector is used to recognize the face image by the help of k nearest neighbors classifier (KNN). The edge responses and the relationship among pixels are very important and play the main role for improving the face recognition accuracy. Therefore, this work presents the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed HODF algorithm is conducted on several publicly available databases and observed promising recognition rates.
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Ongkittikul, Surachai, Wachirapong Kesjindatanawaj, and Sanun Srisuk. "Multi-Window and Line Scan Histogram Features for Bilateral Filtering." Applied Mechanics and Materials 781 (August 2015): 547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.781.547.
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Bilateral filtering is the crucial process to enhance the image. This paper aims to improve the bilateral filtering base on the multi-window and line scan histogram scheme. The multi-windows histogram has been introduced to solve the problem when apply to large image by using a number of the window histogram with different weight to estimate the domain filtering function. Anyway, the complexity of this algorithm is increase by multiply of the number ofmwindows histogram that use for estimating the domain filtering. To improve this, our algorithm that based on the multi-windows histogram is proposed which can reduce the complexity of the filtering from O(mB) to O(m+B). Also, our algorithm uses multi-line scan histogram extraction which adapted from dual line scan histogram extraction to reduce the complexity. The experiments show the new algorithm has slightly increase for filtering when increase the number of the window histogramm.
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Han, Xian-Hua, and Yen-Wei Chen. "Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/241396.
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We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.
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Sinaga, Anita Sindar. "Texture Features Extraction of Human Leather Ports Based on Histogram." Indonesian Journal of Artificial Intelligence and Data Mining 1, no. 2 (2018): 92. http://dx.doi.org/10.24014/ijaidm.v1i2.6084.
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Skin problems general are distinguished on healthy and unhealthy skin. Based on the pores, unhealthy skin: dry, moist or oily skin. Skin problems are identified from the image capture results. Skin image is processed using histogram method which aim to get skin type pattern. The study used 7 images classified by skin type, determined histogram, then extracted with features of average intensity, contrast, slope, energy, entropy and subtlety. Specified skin type reference as a skin test comparator. The histogram-based skin feature feature aims to determine the pattern of pore classification of human skin. The results of the 1, 2, 3 leaf image testing were lean to normal skin (43%), 4, 5, tends to dry skin (29%), 6.7 tend to oily skin (29%). Percentage of feature-based extraction of histogram in image processing reaches 90-95%.
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Husain, Nursuci Putri, and Nurseno Bayu Aji. "Pemanfaatan Histogram Equalization pada Local Tri Directional Pattern untuk Sistem Temu Kembali Citra." SPECTA Journal of Technology 4, no. 1 (2020): 49–58. http://dx.doi.org/10.35718/specta.v4i1.164.
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Abstract
 
 Local tri-directional pattern (LtriDP) is a method of extracting local intensity features from each pixel based on direction. However, this method has not been able to provide good performance in extracting features for image retrieval. One reason that makes image retrieval performance worse is the effect of lighting. Lighting can cause large variations between images. This study proposed utilization of Histogram Equalization (HE). Histogram equalization is a functional method of stretching gray degrees and expanding image contrast. This will make variations in the gray level of the original image can be controlled. There are several main stages in this study, firstly query image and image dataset will be preprocessed with histogram equalization. After that, the image is extracted by a tri-directional pattern and magnitude pattern are searched. A tri-directional pattern will produce two histograms, while a magnitude pattern produces one histogram. The three histograms are combined or joint histogram is performed. Histogram that has been joint is a feature vector. The feature vector will be calculated using a similarity measurement Canberra. After that, an image similar to the query image will be obtained. The experiment was conducted using 3 face datasets namely ORL, BERN, and YALE. The average recall value was 0.422 for the ORL dataset, 0.50 for the BERN dataset, and 0.63 for the YALE dataset. The evaluation show, the proposed method can be used as a process of improving the quality of image datasets in the image retrieval system.
 Keywords: Image retrieval system, Local tri-directional pattern, Streching Image, Histogram Equalization, Similarity Measurement Canberra.
 Abstrak
 
 Local tri-directional pattern (LtriDP) merupakan salah satu metode ekstraksi fitur intensitas lokal dari setiap piksel berdasarkan arah. Namun, metode ini belum mampu memberikan performa yang baik dalam mengekstrak fitur untuk temu kembali citra. Salah satu alasan yang membuat performa temu kembali citra tidak baik adalah pengaruh pencahayaan. Pencahayaan dapat menyebabkan variasi besar antar citra. Penelitian ini mengusulkan pemanfaatan Histogram Equalization (HE). HE merupakan metode fungsional dalam peregangan derajat keabuan dan memperluas kontras citra. Hal ini akan membuat variasi level keabuan dari citra asli dapat terkendali. Ada beberapa tahapan utama dalam penelitian ini, yang pertama citra query dan citra dataset akan terlebih dahulu di preprocessing dengan histogram equalization. Setelah itu, citra tersebut diekstrak fiturnya, dicari pola tri-directional dan pola magnitude. Pola tri-directional akan menghasilkan dua histogram, sedangkan pola magnitude menghasilkan satu histogram. Ketiga histogram tersebut kemudian disatukan atau dilakukan joint histogram. Histogram yang telah dijoint merupakan vektor fitur. Vektor fitur tersebut akan dihitung rankingnya menggunakan pengukuran jarak canberra. Setelah itu, akan didapatkan citra yang mirip dengan citra query. Uji coba dilakukan dengan menggunakan 3 dataset wajah yaitu ORL, BERN, dan YALE. Nilai rata-rata recall yang di dapatkan 0,422 untuk dataset ORL, 0,50 untuk dataset BERN, dan 0,63 untuk dataset YALE. Dari hasil evaluasi tersebut, dapat disimpulkan metode yang diusulkan dapat digunakan sebagai proses peningkatan kualitas dataset citra pada system temu kembali citra.
 Keywords: Sistem Temu Kembali Citra, Local tri-directional pattern, Peregangan Kontras, Histogram Equalization, Perhitungan Jarak Canberra.
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Hua, Ji-Zhao, Guang-Hai Liu, and Shu-Xiang Song. "Content-Based Image Retrieval Using Color Volume Histograms." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (2019): 1940010. http://dx.doi.org/10.1142/s021800141940010x.
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Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.
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Hang, Yuan. "Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/9917538.
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In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. In this paper, we illustrate a method which involves the combination of the deep features with the conventional features together to form a hybrid feature space. Several image enhancement techniques, such as histogram equalization, Laplacian operator, logarithm transform, and Gamma correction, are undertaken to improve the quality and characteristics of the image before feature extraction. Among these methods, applying histogram equalization not only improves the brightness and contrast of the image but also achieves the highest classification accuracy at 69.8%. We extract features such as histograms of oriented gradients, local binary pattern, SIFT, and SURF and combine them with deep features of residual generative adversarial network. We compare the ResNet18, a residual convolutional neural network with 18 layers, with the Res-GAN, a residual generative adversarial network. The experimental result shows that Res-GAN outperforms the former model. Besides, we fuse SURF with deep features with a random forest model as a classifier, which achieves 95% accuracy.
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Ayan, Mehmet, O. Ayhan Erdem, and Hasan Şakir Bilge. "Multi-Featured Content-Based Image Retrieval Using Color and Texture Features." Applied Mechanics and Materials 850 (August 2016): 136–43. http://dx.doi.org/10.4028/www.scientific.net/amm.850.136.
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Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.
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Liu, Guang-Hai, and Zhao Wei. "Image Retrieval Using the Fused Perceptual Color Histogram." Computational Intelligence and Neuroscience 2020 (November 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/8876480.
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Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.
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Liu, Chiang Lung, and Hsing Han Liu. "Reliable Detection of Histogram Shift-Based Steganography Using Payload Invariant Features." Applied Mechanics and Materials 284-287 (January 2013): 3517–21. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3517.
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Reversible data hiding techniques can completely recover the cover images after extracting the secret message from the stego images and become a hot research topic recently. The histogram shift-based steganography, which is a kind of reversible data hiding technique, has good performance on imperceptibility. However, it also produces obvious features in the histograms of stego images. In this paper, we propose a steganalysis method based on the payload invariant features to detect the histogram shift-based steganography proposed by Ni et al. In the proposed steganalysis method, the minimum of the sum of the proposed features is first obtained, which is then compared with a predefined threshold to determine an image is a stego or a cover one. Experimental results show that the proposed steganalysis method can provide very high detection accuracy (about 98%) in various payload cases. Compared with the other steganalysis method, the proposed method can provide better detection performance under different embedding ratios and, therefore, is more reliable for detection of the histogram sift-based steganography.
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Porebski, Alice, Vinh Truong Hoang, Nicolas Vandenbroucke, and Denis Hamad. "Combination of LBP Bin and Histogram Selections for Color Texture Classification." Journal of Imaging 6, no. 6 (2020): 53. http://dx.doi.org/10.3390/jimaging6060053.
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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.
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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 331. http://dx.doi.org/10.11591/tijee.v15i2.1546.
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Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the down-sampled GMIs and apply Histogram of Oriented Gradient to form the features. The experimental results on the FERET face database show the effectiveness of our methods.
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Montazeri, Mitra. "Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features." International Journal of Image and Graphics 19, no. 04 (2019): 1950020. http://dx.doi.org/10.1142/s0219467819500207.
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In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.
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Zhang, Guodong, Peilin Jiang, Kazuyuki Matsumoto, Minoru Yoshida, and Kenji Kita. "Reidentification of Persons Using Clothing Features in Real-Life Video." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5834846.
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Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification.
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Omar, Herman Kh, and Nada E. Tawfiq. "Face Recognition Based on Histogram Equalization and LBP Algorithm." Academic Journal of Nawroz University 8, no. 3 (2019): 33. http://dx.doi.org/10.25007/ajnu.v8n3a394.
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In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image.
 In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database.
 The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table.
 Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).
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Raza, Ahmad, Tabassam Nawaz, Hassan Dawood, and Hussain Dawood. "Square texton histogram features for image retrieval." Multimedia Tools and Applications 78, no. 3 (2018): 2719–46. http://dx.doi.org/10.1007/s11042-018-5795-x.
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Chun, Jun Chul, and Wong Gi Kim. "Textile Image Retrieval Using Composite Feature Vectors of Color and Wavelet Transformed Textural Property." Applied Mechanics and Materials 333-335 (July 2013): 822–27. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.822.
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It is known that wavelet transform provides very useful feature values in analyzing various types of images. This paper presents a novel approach for content-based textile image retrieval which uses composite feature vectors of low-level color feature from spatial domain and second-order statistic features from wavelet-transformed sub-band coefficients. Even though color histogram itself is efficient and most used signature for CBIR, it is unable to carry local spatial information of pixel and generate inaccurate retrieval results especially in large image data set. In this paper, we extract texture features such as contrast, homogeneity, ASM(angular-second momentum) and entropy from decomposed sub-band images by wavelet transform and utilize these multiple feature vector to retrieve textile images combining with color histogram. From the experimental results it is proven that the proposed approach is efficiently retrieve the desired images from a large set of textile image database.
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Srikanth M. V., V. V. K. D. V. Prasad, and K. Satya Prasad. "An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 3 (2020): 60–96. http://dx.doi.org/10.4018/ijcini.2020070104.
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In this article, an attempt is made to diagnose brain diseases like neoplastic, cerebrovascular, Alzheimer's, and sarcomas by the effective fusion of two images. The two images are fused in three steps. Step 1. Segmentation: The images are segmented on the basis of optimal thresholding, the thresholds are optimized with an improved firefly algorithm (pFA) by assuming Renyi entropy as an objective function. Earlier, image thresholding was performed with a 1-D histogram, but it has been recently observed that a 2-D histogram-based thresholding is better. Step 2: the segmented features are extracted with the scale invariant feature transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: The fusion rules are made on the basis of an interval type-2 fuzzy set (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark image fusion data sets and has proven better in all measuring parameters.
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Xu, Bing. "Retrieval Model Based on Image Content Features." Applied Mechanics and Materials 635-637 (September 2014): 1035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1035.
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This paper presents fuzzy color histogram feature-based image retrieval method and texture spectrum fuzzy histogram feature analyzes the image database indexing techniques and the introduction of the experimental system for an improved method of fuzzy indexes. Algorithm reflects the underlying characteristics of high-level concepts and integration, relevance feedback and machine learning mechanism combining ideas. In this paper, the algorithm full processing power of computer systems, has a certain reference value and practical significance.
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Su, Ching Hung, Huang Sen Chiu, Mohd Helmy A. Wahab, Tsai Ming Hsiehb, You Chiuan Li, and Jhao Hong Lin. "Images Retrieval Based on Integrated Features." Applied Mechanics and Materials 543-547 (March 2014): 2292–95. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2292.
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We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.
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SHAN, YING, HARPREET S. SAWHNEY, and ART POPE. "CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (2005): 551–64. http://dx.doi.org/10.1142/s0218001405004149.
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We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.
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Huque, Abu Sayeed Ahsanul, Mainul Haque, Haidar A. Khan, Abdullah Al Helal, and Khawza I. Ahmed. "Comparative Study of KNN, SVM and SR Classifiers in Recognizing Arabic Handwritten Characters Employing Feature Fusion." Signal and Image Processing Letters 1, no. 2 (2019): 1–10. http://dx.doi.org/10.31763/simple.v1i2.1.
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This paper evaluates and compares the performance of K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Sparse Representation Classifier (SRC) for recognition of isolated Arabic handwritten characters. The proposed framework converts the gray-scale character image to a binary image through Otsu thresholding, and size-normalizes the binary image for feature extraction. Next, we exploit image down-sampling and the histogram of image gradients as features for image classification and apply fusion (combination) of these features to improve the recognition accuracy. The performance of the proposed system is evaluated on Isolated Farsi/Arabic Handwritten Character Database (IFHCDB) – a large dataset containing gray scale character images. Experimental results reveal that the histogram of gradient consistently outperforms down-sampling based features, and the fusion of these two feature sets achieves the best performance. Likewise, SRC and SVM both outperform KNN, with the latter performing the best among the three. Finally, we achieved a commanding accuracy of 93.71% in character recognition with fusion of features classified by SVM, where 92.06% and 91.10% is achieved by SRC and KNN respectively.
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Gupta, Sheifali, Gurleen Kaur, Deepali Gupta, and Udit Jindal. "Brazilian Coins Recognition Using Histogram of Oriented Gradients Features." Journal of Computational and Theoretical Nanoscience 16, no. 10 (2019): 4170–78. http://dx.doi.org/10.1166/jctn.2019.8498.
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This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.
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Fan, Deng Ping, Juan Wang, and Xue Mei Liang. "Improving Image Retrieval Using the Context-Aware Saliency Areas." Applied Mechanics and Materials 734 (February 2015): 596–99. http://dx.doi.org/10.4028/www.scientific.net/amm.734.596.
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The Context-Aware Saliency (CA) model—is a new model used for saliency detection—has strong limitations: It is very time consuming. This paper improved the shortcoming of this model namely Fast-CA and proposed a novel framework for image retrieval and image representation. The proposed framework derives from Fast-CA and multi-texton histogram. And the mechanisms of visual attention are simulated and used to detect saliency areas of an image. Furthermore, a very simple threshold method is adopted to detect the dominant saliency areas. Color, texture and edge features are further extracted to describe image content within the dominant saliency areas, and then those features are integrated into one entity as image representation, where image representation is so called the dominant saliency areas histogram (DSAH) and used for image retrieval. Experimental results indicate that our algorithm outperform multi-texton histogram (MTH) and edge histogram descriptors (EHD) on Corel dataset with 10000 natural images.
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Sima, Haifeng, Aizhong Mi, Zhiheng Wang, and Youfeng Zou. "Objectness Supervised Merging Algorithm for Color Image Segmentation." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3180357.
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Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.
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Lu, Ming, and Shaozhang Niu. "Detection of Image Seam Carving Using a Novel Pattern." Computational Intelligence and Neuroscience 2019 (November 11, 2019): 1–15. http://dx.doi.org/10.1155/2019/9492358.
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Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.
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Esther Ratna, T., and N. Subash Chandra. "Binary Plane Technique Based Color Quantization for Content Based Image Retrieval." International Journal of Engineering & Technology 7, no. 3.1 (2018): 124. http://dx.doi.org/10.14419/ijet.v7i3.1.16814.
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Extracting accurate informative file from a high volume of graphic files is a challenging task. This paper focus on presenting a new color indexing approach using the histogram features. Two histogram features like maximum color histogram and minimum color histogram are computed and are vector quantized to constitute a feature vector. Bit plane technique is used to map these features based upon it value at the respective position. The ultimate goal of any retrieval method is to attain higher precision within a short span of time that could be achieved if the data is in compressed to accomplish this the image is compressed using binary plane technique. The result analysis depicts the performance of the proposed approach under lossy and lossless modes and found that when operated in lossy it attain effective precision rate in a speculated amount of time.
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Yang, Liang, Tiegang Gao, Yan Xuan, and Hang Gao. "Contrast Modification Forensics Algorithm Based on Merged Weight Histogram of Run Length." International Journal of Digital Crime and Forensics 8, no. 2 (2016): 27–35. http://dx.doi.org/10.4018/ijdcf.2016040103.
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A novel image forensic algorithm against contrast modification based on merged weight histogram of run length is proposed. In the proposed algorithm, the run length histogram features were firstly extracted, and then those of different orientation were subsequently merged; after normalization of the prior features, the authors calculated leaps in the histogram numerically; lastly, the generated features of authentic and tampered images were trained by a SVM classifier. Large amounts of experiments show that, the proposed algorithm has low cost of computation complexity, compared with some existing scheme, and it has better performance with many test databases, furthermore, the proposed algorithm can effectively detect local contrast modification of image.
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ZAMPERONI, P. "FEATURE EXTRACTION BY RANK-ORDER FILTERING FOR IMAGE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 02 (1988): 301–19. http://dx.doi.org/10.1142/s0218001488000194.
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The aim of this paper is to outline a unified approach to feature extraction for segmentation purposes by means of the rank-order filtering of grey values in a neighbourhood of each pixel of a digitized image. In the first section an overview of rank-order filtering for image processing is given, and a fast histogram algorithm is proposed. Section 2 deals with the extraction of a “locally most representative grey value”, defined as the maximum of the local histogram density function. In Section 3 several textural features are described, which can be extracted from the local histogram by means of rank-order filtering, and their properties are discussed. Section 4 formulates some general requirements to be met by the process of image segmentation, and describes a method based upon the features introduced in the former sections. In the last section some experimental results applied to aerial views obtained with the segmentation method of Sect. 4 are reported. These test images have been analyzed within the scope of an investigation centered on terrain recognition for agricultural and ecological purposes.
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Wang, Hui, Gang Liu, and Hong Chang Ke. "An Improved Direction Index Histogram Handwriting Identification Method." Advanced Materials Research 846-847 (November 2013): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1230.
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Based on direction index histogram (DIH) handwriting identification method, a new improved DIH handwriting identification algorithm is proposed. Firstly handwriting image which is prepared to test is pre-treat, then binary and removing noise the normalized image can be obtain. The obtained features of distance are divided into two factors: writing influence factor and character influence factor, then compared the pre-test images with samples of handwriting images according to the features of the images. Experimental results show that the handwriting identification algorithm proposed by this paper has better recognition rate than DIH algorithm, and obtain better result.
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Sandhu, Amanbir, and Aarti Kochhar. "Content Based Image Retrieval using Texture, Color and Shape for Image Analysis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 149–52. http://dx.doi.org/10.24297/ijct.v3i1c.2768.
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Content- Based Image Retrieval(CBIR) or QBIR is the important field of research..Content Based Image retrieval has gained much popularity in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties over color, texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output with a single feature
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Wang, G. H., H. B. Wang, W. F. Fan, Y. Liu, and C. Chen. "CHANGE DETECTION OF HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON ADAPTIVE FUSION OF MULTIPLE FEATURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1689–94. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1689-2018.
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In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.
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Christanti Mawardi, Viny, Yoferen Yoferen, and Stéphane Bressan. "Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods." E3S Web of Conferences 188 (2020): 00026. http://dx.doi.org/10.1051/e3sconf/202018800026.
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Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.
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Islam, Md Saiful, Md Emdadul Haque, and Md Ekramul Hamid. "Multidimensional Markov Stationary Feature for Image Retrival Systems." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 113–22. http://dx.doi.org/10.3329/rujse.v44i0.30396.
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Markov Stationary Features (MSF) not only considers the distribution of colors like histogram method does, also characterizes the spatial co-occurrence of histogram patterns. However, handling large scale database of images, simple MSF method is not sufficient to discriminate the images. In this paper, we have proposed a robust content based image retrieval algorithm that enhances the discriminating capability of the original MSF. The proposed Multidimensional MSF (MMSF) algorithm extends the MSF by generating multiple co-occurrence matrices with different quantization levels of an image. Publicly available WANG1000 and Corel10800 databases are used to evaluate the performance of the proposed algorithm. The experimental result justifies the effectiveness of the proposed method.
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A. Jain, Sajan, N. Shobha Rani, and N. Chandan. "Image Enhancement of Complex Document Images Using Histogram of Gradient Features." International Journal of Engineering & Technology 7, no. 4.36 (2018): 780. http://dx.doi.org/10.14419/ijet.v7i4.36.24244.
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Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination gradient is foreseen. In the proposed research, ancient document images of UMIACS Tobacco 800 database are considered for removal of marginal noise. The proposed technique carries out the block wise interpretation of document contents to remove the marginal noise that is present usually at the borders of images. Further, Hu moment’s features are computed for the detection of marginal noise in every block. An empirical analysis is carried out for classification of blocks into noisy or non-noisy and the outcomes produced by algorithm are satisfactory and feasible for subsequent analysis.
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Thomas, G., A. Manickavasagan, L. Khriji, and R. Al-Yahyai. "Contrast Enhancement Using Brightness Preserving Histogram Equalization Technique for Classification of Date Varieties." Journal of Engineering Research [TJER] 10, no. 2 (2014): 55. http://dx.doi.org/10.24200/tjer.vol11iss1pp55-63.
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Computer vision technique is becoming popular for quality assessment of many products in food industries. Image enhancement is the first step in analyzing the images in order to obtain detailed information for the determination of quality. In this study, Brightness preserving histogram equalization technique was used to enhance the features of gray scale images to classify three date varieties (Khalas, Fard and Madina). Mean, entropy, kurtosis and skewness features were extracted from the original and enhanced images. Mean and entropy from original images and kurtosis from the enhanced images were selected based on Lukka's feature selection approach. An overall classification efficiency of 93.72% was achieved with just three features. Brightness preserving histogram equalization technique has great potential to improve the classification in various quality attributes of food and agricultural products with minimum features.
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Park, Sohee, Hansung Lee, Jang-Hee Yoo, Geonwoo Kim, and Soonja Kim. "Partially Occluded Facial Image Retrieval Based on a Similarity Measurement." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/217568.
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We present a partially occluded facial image retrieval method based on a similarity measurement for forensic applications. The main novelty of this method compared with other occluded face recognition algorithms is measuring the similarity based on Scale Invariant Feature Transform (SIFT) matching between normal gallery images and occluded probe images. The proposed method consists of four steps: (i) a Self-Quotient Image (SQI) is applied to input images, (ii) Gabor-Local Binary Pattern (Gabor-LBP) histogram features are extracted from the SQI images, (iii) the similarity between two compared images is measured by using the SIFT matching algorithm, and (iv) histogram intersection is performed on the SIFT-based similarity measurement. In experiments, we have successfully evaluated the performance of the proposed method with the commonly used benchmark database, including occluded facial images. The results show that the correct retrieval ratio was 94.07% in sunglasses occlusion and 93.33% in scarf occlusion. As such, the proposed method achieved better performance than other Gabor-LBP histogram-based face recognition algorithms in eyes-hidden occlusion of facial images.
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Bhairannawar, Satish S., K. B. Raja, and K. R. Venugopal. "An Efficient Reconfigurable Architecture for Fingerprint Recognition." VLSI Design 2016 (July 28, 2016): 1–22. http://dx.doi.org/10.1155/2016/9532762.
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The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.
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Aljanabi, Mohammed Abdulameer, Zahir M. Hussain, and Song Feng Lu. "An Entropy-Histogram Approach for Image Similarity and Face Recognition." Mathematical Problems in Engineering 2018 (July 9, 2018): 1–18. http://dx.doi.org/10.1155/2018/9801308.
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Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between image intensity values. The joint histogram is based mainly on selecting a set of local pixel features to construct a multidimensional histogram. The proposed approach incorporates the concepts of entropy and a modified 1D version of the 2D joint histogram of the two images under test. Two entropy measures were considered, Shannon and Renyi, giving a rise to two joint histogram-based, information-theoretic similarity measures: SHS and RSM. The proposed methods have been tested against powerful Zernike-moments approach with Euclidean and Minkowski distance metrics for image recognition and well-known statistical approaches for image similarity such as structural similarity index measure (SSIM), feature similarity index measure (FSIM) and feature-based structural measure (FSM). A comparison with a recent information-theoretic measure (ISSIM) has also been considered. A measure of recognition confidence is introduced in this work based on similarity distance between the best match and the second-best match in the face database during the face recognition process. Simulation results using AT&T and FEI face databases show that the proposed approaches outperform existing image recognition methods in terms of recognition confidence. TID2008 and IVC image databases show that SHS and RSM outperform existing similarity methods in terms of similarity confidence.
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Zhou, Guodong, Huailiang Zhang, and Raquel Martínez Lucas. "Compressed sensing image restoration algorithm based on improved SURF operator." Open Physics 16, no. 1 (2018): 1033–45. http://dx.doi.org/10.1515/phys-2018-0124.
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Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.
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Kaur, Gagandeep, and Rajeev Kumar Dang. "Feature Based Comparison of Text Based Image Retrieval and Context Based Image Retrieval Images." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 6–11. http://dx.doi.org/10.51983/ajeat-2018.7.2.965.
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Image processing is a field to process the images according to horizontal and vertical axis to form some useful results. It deals with edge detection, image compression, noise removal, image segmentation, image identification, image retrieval and image variation etc. Customarily, there are two techniques i.e. text based image retrieval and content based image retrieval that are used for retrieving the image according to features and providing color to all pixel pairs. The system retrieval that is based on TBIR assists to recover an image from the database using annotations. CBIR extorts images to form a hefty degree database using the visual contents of an original image that is called low level features or features of an image. These visual features are extracted using feature extraction and then match with the input image. Histogram, color moment, color correlogram, Gabor filter and wavelet transform are various CBIR techniques that can be used autonomously or pooled to acquire enhanced consequences. This paper states about a novel technique for fetching the images from the image database using two low level features namely color based feature and texture based features. Two techniques- one is color correlogram (for color indexing) and another is wavelet transform (for texture processing) has also been introduced.
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Pandey, Adi Sugita, I. Gede Pasek Suta Wijaya, and Fitri Bimantoro. "Haar Wavelet Untuk Ekstraksi Fitur Energi, Standar Deviasi, Dan Histogram Dalam Sistem Temu Kembali Citra." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 2, no. 1 (2020): 40–49. http://dx.doi.org/10.29303/jtika.v2i1.67.
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Image retrieval initially uses a query in the form of text to search for images in the database. Image search using text query has a weakness because of the limited description of information stored or given by humans to the metadata on an inconsistent image that greatly affects the duration of searching an image in a database. Content based image retrieval (CBIR) is an image processing application to find the image sought in a large image database based on a query or user request. CBIR technique utilizes features that exist in images, namely color, texture, and shape. These features will be used as a basis for searching images in an image database. In this study the authors used the Haar wavelet method and histogram to look for texture and color features in the image. Then the features found are matched with features stored in the database using the Euclidian distance method. In this study the authors used the Corel dataset as research material. The dataset used is classified into 3 categories: bus, animal and sunset. Each category consists of 100 images where 70% are training images and 30% are test images.
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Kim, Bubryur, Ronnie O. Serfa Juan, Dong-Eun Lee, and Zengshun Chen. "Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image." Applied Sciences 11, no. 18 (2021): 8388. http://dx.doi.org/10.3390/app11188388.
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Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.
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Chen, Yan-Hong, Chin-Chen Chang, Chia-Chen Lin, and Cheng-Yi Hsu. "Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization." Symmetry 11, no. 1 (2018): 21. http://dx.doi.org/10.3390/sym11010021.
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In this paper, we propose a content-based image retrieval (CBIR) approach using color and texture features extracted from block truncation coding based on binary ant colony optimization (BACOBTC). First, we present a near-optimized common bitmap scheme for BTC. Then, we convert the image to two color quantizers and a bitmap image-utilizing BACOBTC. Subsequently, the color and texture features, i.e., the color histogram feature (CHF) and the bit pattern histogram feature (BHF) are extracted to measure the similarity between a query image and the target image in the database and retrieve the desired image. The performance of the proposed approach was compared with several former image-retrieval schemes. The results were evaluated in terms of Precision-Recall and Average Retrieval Rate, and they showed that our approach outperformed the referenced approaches.
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HUANG, WEI, and HONGTAO LU. "AUTOMATIC DEFECT CLASSIFICATION OF TFT-LCD PANELS WITH SHAPE, HISTOGRAM AND COLOR FEATURES." International Journal of Image and Graphics 13, no. 03 (2013): 1350011. http://dx.doi.org/10.1142/s0219467813500113.
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In this paper, an automatic defect classification algorithm for thin film transistor liquid crystal display (TFT-LCD) manufacturing is proposed. Each sample of defect data contains three images: the original image, the defect shape image and the circuit zone image. A set of features including shape, histogram and color is extracted. Some common classifiers were tested in the experiments and Linear-SVM (Linear Surport Vector Machine) was chosen in practical manufacturing. A novel LBP-E feature considering intensity equality proposed in this paper is compared to other original rotation invariant LBP (Local Binary Pattern) features. The experimental results show that our method can generate a better result with a relatively low dimension number.