Academic literature on the topic 'Image Histogram Features'

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.