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Journal articles on the topic 'Color Correlogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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1

Mao, Yan Fen, and Ming Chen. "Correlogram-Based Perceptual Similarity in Vehicle Probabilistic Tracking." Applied Mechanics and Materials 121-126 (October 2011): 3860–64. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3860.

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This paper addresses the problem of dissimilarity measurement and incorporates an auto-correlogram operator into probabilistic tracking. Perceptual similarity measurement based upon color correlogram is proposed to weigh the candidates and obtain the expectation state vector of tracked vehicles. The object color is represented in perceptually better-organized HSV color space, which produced improvement over the original method that used in the RGB color space. Properly quantized HSV color space improved the efficiency of auto-correlogram computation and robustness to changes in color content a
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TAI, XIAO-YING, LI-DONG WANG, QIN CHEN, REN FUJI, and KITA KENJI. "A NEW METHOD OF MEDICAL IMAGE RETRIEVAL BASED ON COLOR–TEXTURE CORRELOGRAM AND GTI MODEL." International Journal of Information Technology & Decision Making 08, no. 02 (2009): 239–48. http://dx.doi.org/10.1142/s0219622009003363.

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This paper presents a method for endoscopic image retrieval based on color–texture correlogram and Generalized Tversky's Index (GTI) model. First we define a new image feature named color–texture correlogram, which is the extension of color correlogram. The texture image extracted by texture spectrum algorithm is combined with color feature vector, and then we calculate the spatial correlation of color–texture feature vector. Similarity metric is also the key technology during domain of image retrieval, GTI model is used in medical image retrieval for similarity metric, and the technique of re
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3

Abdul-samad, Sarmad. "COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW." Iraqi Journal for Computers and Informatics 45, no. 1 (2019): 15–19. http://dx.doi.org/10.25195/ijci.v45i1.45.

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Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vec
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Choi, J. H., G. Y. Song, and J. W. Lee. "Road identification in monocular color images using random forest and color correlogram." International Journal of Automotive Technology 13, no. 6 (2012): 941–48. http://dx.doi.org/10.1007/s12239-012-0095-3.

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Choi, Ji-Hye, Gwang-Yul Song, and Joon-Woong Lee. "Road Extraction Based on Random Forest and Color Correlogram." Journal of Institute of Control, Robotics and Systems 17, no. 4 (2011): 346–52. http://dx.doi.org/10.5302/j.icros.2011.17.4.346.

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Siddesha, S., and S. K. Niranjan. "Color Based Oil Palm Crop Bunch Grading Using Probabilistic Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4141–44. http://dx.doi.org/10.1166/jctn.2020.9034.

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This work aims at grading the oil palm crop bunch in to three categories unripe, ripe and overripe. Different color feature models like color histogram, color moments, color correlogram and color coherence vector are used to extract the color features of the crop bunch. Oil palm crop bunches are classified into above mentioned grades using Probabilistic Neural Network. Experimentation is carried out using image dataset of 300 RGB images across three categories. An accuracy of 98.33% is achieved with 70% training, 10% validation and 20% testing for Color Coherence Vector features.
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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
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Vinayak, Vandana, and Sonika Jindal. "CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding." International Journal of Computer Applications 161, no. 9 (2017): 1–7. http://dx.doi.org/10.5120/ijca2017913282.

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9

Yang, Fu-ping, and Mei-li Hao. "Effective Image Retrieval Using Texture Elements and Color Fuzzy Correlogram." Information 8, no. 1 (2017): 27. http://dx.doi.org/10.3390/info8010027.

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Ponnusamy, R., S. Sathiamoorthy, and R. Visalakshi. "An efficient method to classify GI tract images from WCE using visual words." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5678. http://dx.doi.org/10.11591/ijece.v10i6.pp5678-5686.

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The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this
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11

Ji, Meng, Xiao Ming Sun, Xin Chen, Li Jun Qi, Lei Bao, and Lei Huang. "A Image Retrival Method with Multi-Features Based on Dempster-Shafer Theory." Advanced Materials Research 981 (July 2014): 360–63. http://dx.doi.org/10.4028/www.scientific.net/amr.981.360.

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Image content based retrieval is an important research area with application to digital libraries and multimedia databases. Its image characterization and similarity measure must closely follow perceptual characteristics. In this work, a new image retrieval method is proposed by combining color features and texture features based on Dempster-Shafer (D-S) theory. In this proposed method, Multi-color features included color histogram, color moment and color correlogram are used for color analysis, and gabor wavelet features is employed for texture analysis. Then these features are modeled as a m
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Murala, Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. "Multiresolution LBP Correlogram for Texture Image Indexing and Retrieval." Advanced Materials Research 403-408 (November 2011): 908–14. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.908.

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A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of LBPs from these particular textures. Eventually, feature vectors are constructed by making into play the auto-correlatio
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S. M., Manimegalai, and Dr Ramaprabha T. "IMAGE CLASSIFICATIONS ON COVID 19 CXR IMAGES USING AUTO COLOR CORRELOGRAM FILTER." Indian Journal of Computer Science and Engineering 12, no. 5 (2021): 1288–301. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205174.

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Zhao, Hong, Hongxia Wang, and Muhammad Khurram Khan. "Steganalysis for palette-based images using generalized difference image and color correlogram." Signal Processing 91, no. 11 (2011): 2595–605. http://dx.doi.org/10.1016/j.sigpro.2011.05.015.

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15

Zhao, Qi, and Hai Tao. "A motion observable representation using color correlogram and its applications to tracking." Computer Vision and Image Understanding 113, no. 2 (2009): 273–90. http://dx.doi.org/10.1016/j.cviu.2008.10.007.

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Song, Juw-Han. "Content-based Image Retrieval using Histogram and Motif Correlogram of HSV Color Images." Journal of Korean Institute of Information Technology 14, no. 5 (2016): 181. http://dx.doi.org/10.14801/jkiit.2016.14.5.181.

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Sujeeta, Yadav*1 &. Prof. Achint Chugh2. "TO STUDY ANALYSIS OF COLOUR AND TEXTURE BASED IMAGE RETRIEVAL." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 7 (2019): 27–34. https://doi.org/10.5281/zenodo.3264826.

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Because of the advancement and improvement in innovation and web ,with rapid throughout the previous couple of years and the accessibility of a huge computerized picture accumulation, effective picture recovery frameworks are required and productive CBIR framework serve that need. In this, we will give an inquiry picture to the framework and framework will look for the comparative picture as that of the given question picture and recovery the picture which is most like that of the inquiry picture. For this, we will separate the highlights of question picture dependent on its shading and surfac
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Malviya, Ashwini V., and Siddharth A. Ladhake. "Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram." Procedia Computer Science 79 (2016): 383–90. http://dx.doi.org/10.1016/j.procs.2016.03.050.

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19

Said, Nur Sholehah Mat, Hizmawati Madzin, Siti Khadijah Ali, and Ng Seng Beng. "Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1523. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1523-1533.

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In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research
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Sholehah, Nur, Hizmawati Madzin, Siti Khadijah Ali, and Ng Seng Beng. "Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1523–33. https://doi.org/10.11591/ijeecs.v24.i3.pp1523-1533.

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In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research
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21

Jalal, Anand Singh, and Jitesh Kumar Bhatia. "Pixel based Hybrid Copy Move Image Forgery Detection using Zernike Moments and Auto Color Correlogram." International Journal of Information and Computer Security 11, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijics.2019.10036171.

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22

Dhyanti, Yustina, Khairul Munadi, and Fitri Arnia. "Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak." Jurnal Rekayasa Elektrika 12, no. 3 (2016): 104. http://dx.doi.org/10.17529/jre.v12i3.5701.

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Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR). Particularly, we focused on us
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23

Venkataravana, Nayak K. ,., J. S. Arunalatha, and K. R. Venugopal. "IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble." International Journal of Circuits, Systems and Signal Processing 15 (December 22, 2021): 1781–89. http://dx.doi.org/10.46300/9106.2021.15.192.

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Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histog
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24

R., Ponnusamy, Sathiamoorthy S., and Visalakshi R. "An efficient method to classify GI tract images from WCE using visual words." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5678–86. https://doi.org/10.11591/ijece.v10i6.pp5678-5686.

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The digital images made with the wireless capsule endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the center symmetric local binary pattern (CS-LBP) and the auto color correlogram (ACC) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve
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25

Lamba, Monika, Yogita Gigras, and Anuradha Dhull. "Classification of plant diseases using machine and deep learning." Open Computer Science 11, no. 1 (2021): 491–508. http://dx.doi.org/10.1515/comp-2020-0122.

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Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess
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Alhaj, Omar A., Hiba F. Al-Sayyed, Ola M. Charouf, Haitham Jahrami, and Keith Morris. "The Validation and Reliability of the Food and Health Questionnaire to Assess Health and Nutrition Knowledge." Food and Nutrition Bulletin 43, no. 1 (2021): 14–24. http://dx.doi.org/10.1177/03795721211052836.

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Purpose: This study aimed to translate and test the content validity and reliability of an English questionnaire used in the American food and health survey. Methods: The questionnaire was developed using 6 stages, then examined for test–retest. A total of 672 participants were recruited for validation and reliability. Validity test was performed using a correlation coefficient to measure the linear correlation between 2 questions at one given time. Moreover, the Kaiser-Meyer-Olkin, Measure of Sampling Adequacy, and Bartlett’s Test of Sphericity were performed to statistically determine the su
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Koram, Singh* Suman Rani. "CONTENT BASED IMAGE RETRIEVAL ON COLOR, TEXTURE AND SHAPE FEATURES USING DWT AND MODIFIED K-MEANS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 956–66. https://doi.org/10.5281/zenodo.222253.

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Here we proposed algorithms for CBIR system on the basis of texture, shape,and color based feature extraction and matching of color and texture.We used the Discrete Wavelet transform for decomposition of images and clusters calculations using modified K-Means clustering.We extract texture,shape, and color and finaly measure similarity between query image and database image and reduced semnatic gap between local features and global features. Integrated approach retrive more accurate image, reduce semantic gap between local and high level features.The time taken by Modified K-Means is less as co
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Natalia, Friska, Sud Sudirman, Daniel Ruslim, and Ala Al-Kafri. "Lumbar spine MRI annotation with intervertebral disc height and Pfirrmann grade predictions." PLOS ONE 19, no. 5 (2024): e0302067. http://dx.doi.org/10.1371/journal.pone.0302067.

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Many lumbar spine diseases are caused by defects or degeneration of lumbar intervertebral discs (IVD) and are usually diagnosed through inspection of the patient’s lumbar spine MRI. Efficient and accurate assessments of the lumbar spine are essential but a challenge due to the size of the clinical radiologist workforce not keeping pace with the demand for radiology services. In this paper, we present a methodology to automatically annotate lumbar spine IVDs with their height and degenerative state which is quantified using the Pfirrmann grading system. The method starts with semantic segmentat
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Gholam, Ali Montazer, and Davar Giveki. "Content based image retrieval system using clustered scale invariant feature transforms." optik 126 (June 7, 2015): 1695–99. https://doi.org/10.5281/zenodo.14013493.

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The large amounts of image collections available from a variety of sources have posed increasing technical challenges to computer systems to store/transmit and index/manage the image data to make such collections easily accessible.‎ To search and retrieve the expected images from the database a content-based image retrieval (CBIR) system is highly demanded.‎ CBIR extracts features of a query image and try to match them with extracted features from images in the database.‎ This paper introduces two novel methods as image descriptors.‎ The basis of the proposed methods is built u
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Wu, Xian Wei, Yu Bing Yang, and Wen Yang Yu. "Content-Based Medical Image Retrieval System for Color Endoscopic Images." Advanced Materials Research 798-799 (September 2013): 1022–25. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.1022.

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To retrieve endoscopic Images efficiently, we make out several experiments using color histograms feature and color correlograms feature separately. Then integrate color histograms feature and color correlograms feature, and reduce the dimensions of combination features through using SPCA algorithm The results of experiments show that the performance of combination feature is better, and the speed of retrieval becomes higher.
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Dash, Prosanta K., Bing Guo, and Daniel I. Leskovar. "Biostimulants Alleviate Heat Stress in Organic Hydroponic Tomato Cultivation: A Sustainable Approach." HortScience 60, no. 3 (2025): 344–52. https://doi.org/10.21273/hortsci18039-24.

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Heat stresses significantly reduce crop yield by 50% to 70%, irrespective of stress exposure time, plant species, or cultivars, posing substantial challenges for organically growing hydroponic tomatoes in controlled environment conditions. Low productivity in tomatoes is attributed to the inadequate stress tolerance of existing cultivars, which hinders their ability to optimize fruit set and yield. In this study, three biostimulants—Liquid Seaweeds, MycoApply®, and MycoLife—were applied to the leaf surface of tomato plants at 7-day intervals starting 4 weeks after planting. Their effects on pl
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Xielifuguli, Keranmu, Akira Fujisawa, Yusuke Kusumoto, Kazuyuki Matsumoto, and Kenji Kita. "Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/415187.

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People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images
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Agarwal, Megha, and R. P. Maheshwari. "Binary Wavelet Transform Based Histogram Feature for Content Based Image Retrieval." International Journal of Electronics Signals and Systems, July 2011, 68–75. http://dx.doi.org/10.47893/ijess.2011.1014.

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In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database in color as well as gray space. The results analysis of DB1 database illustrates the better average precision and average recall of proposed method in RGB space (73.82%, 44.29%) compared to color histog
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Silpa, Chaitanya, Reddy K.V.Narasimha, and G.Madhavi. "EFFECTIVE SEARCH OF COLOR-SPATIAL IMAGE USING SEMANTIC INDEXING." April 30, 2012. https://doi.org/10.5121/ijcsea.2012.2411.

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Most of the data stored in libraries are in digital form will contain either pictures or video, which is tough to search or browse. Methods which are automatic for searching picture collections made large use of color histograms, because they are very strong to wide changes in viewpoint, and can be calculated trivially. However, color histograms unable to present spatial data, and therefore tend to give lesser results. By using combination of color information with spatial layout we have developed several methods, while retrieving the advantages of histograms. A method computes a given color a
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KHUTWAD, HARSHADA ANAND, and RAVINDRA JINADATTA VAIDYA. "CONTENT BASED IMAGE RETRIEVAL." International Journal of Image Processing and Vision Science, April 2013, 294–99. http://dx.doi.org/10.47893/ijipvs.2013.1058.

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Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color,
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KHUTWAD, HARSHADA ANAND, and RAVINDRA JINADATTA VAIDYA. "CONTENT BASED IMAGE RETRIEVAL." International Journal of Image Processing and Vision Science, April 2013, 294–99. http://dx.doi.org/10.47893/ijipvs.2013.1058.

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Content Based Image Retrieval is an interesting and most emerging field in the area of ‘Image Search’, finding similar images for the given query image from the image database. Current approaches include the use of color, texture and shape information. Considering these features in individual, most of the retrievals are poor in results and sometimes we are getting some non relevant images for the given query image. So, this dissertation proposes a method in which combination of color and texture features of the image is used to improve the retrieval results in terms of its accuracy. For color,
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37

"Detection and Classification of Early Stage Lesions in Diabetic Retinopathy using Color Fundus Images." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 4476–80. http://dx.doi.org/10.35940/ijrte.c6806.098319.

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Detection of lesions and classification of Diabetic Retinopathy (DR) play an important role in day-to-day life. In this proposed system, colour fundus image is pre-processed using morphological operations to recover from noises and it is converted into HSV colorspace. Fuzzy C-Means Clustering algorithm (FCMC) is used for segmenting the early stage lesions such as Microaneurysms (Ma), Haemorrhages (HE) and Exudates. Hybrid features such as colour correlogram and speeded up robust features (surf) are extracted to train the classifier. Cascaded Rotation Forest (CRF) classifier is used for classif
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"The Color Cross-Correlogram for the Robust Image Retrieval in the Size Change of Regions." KIPS Transactions:PartB 9B, no. 6 (2002): 753–58. http://dx.doi.org/10.3745/kipstb.2002.9b.6.753.

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Bhardwaj, Shikha, Gitanjali Pandove, and Pawan Kumar Dahiya. "An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System." Recent Advances in Computer Science and Communications 13 (November 22, 2019). http://dx.doi.org/10.2174/2666255813666191122113801.

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Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparati
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Kavitha, P., G. Ayyappan, Prabhu Jayagopal, et al. "Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach." BMC Bioinformatics 24, no. 1 (2023). http://dx.doi.org/10.1186/s12859-023-05584-7.

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AbstractIntense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, t
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"Prediction of Esophagitis and Z-Line from Wireless Capsule Endoscopy images using Fusion of low-level features." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 6024–28. http://dx.doi.org/10.35940/ijrte.c5568.098319.

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Wireless endoscopy capsule (WCE) pictures areoften used to recognize digestive tract illnesses as they enable immediate GI tract perception. In any case, it requires a clinician's long-lasting review because of an incredible number of pictures delivered by the system. In this manner, it might be valuable to devise an Automatic detection framework to enable clinicians to distinguish abnormal pictures. In this work, it is endeavour to plan an electronic plan intending to distinguish esophagitis in WCE pictures. The Esophagitis in WCE pictures show extraordinary variations in appearance. Scale-In
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Nader, Nermeen, Fatma EL-Zahraa A. EL-Gamal, and Mohammed Elmogy. "Enhanced kinship verification analysis based on color and texture handcrafted techniques." Visual Computer, June 13, 2023. http://dx.doi.org/10.1007/s00371-023-02919-6.

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AbstractNowadays, kinship verification is an attractive research area within computer vision. It significantly affects applications in the real world, such as finding missing individuals and forensics. Despite the importance of this research topic, it still faces many challenges, such as low accuracy and illumination variations. Due to the existence of different classes of feature extraction techniques, different types of information can be extracted from the input data. Moreover, the fusion power produces complementary information that can address kinship verification problems. Therefore, thi
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Alsakar, Yasmin M., Nehal A. Sakr, and Mohammed Elmogy. "An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique." Scientific Reports 14, no. 1 (2024). https://doi.org/10.1038/s41598-024-81143-1.

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AbstractThe rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert’s experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases. Moreover, more information can be extracted from the input images due to different feature extraction techniques. This paper proposes a new system for detec
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P., Gnanasekar, Nagappan A., Sharavanan S., et al. "Investigation on Feature Extraction and Classification of Medical Images." December 21, 2011. https://doi.org/10.5281/zenodo.1076996.

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In this paper we present the deep study about the Bio- Medical Images and tag it with some basic extracting features (e.g. color, pixel value etc). The classification is done by using a nearest neighbor classifier with various distance measures as well as the automatic combination of classifier results. This process selects a subset of relevant features from a group of features of the image. It also helps to acquire better understanding about the image by describing which the important features are. The accuracy can be improved by increasing the number of features selected. Various types of cl
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Samarthrao, Kadam Vikas, and Vandana M. Rohokale. "Enhancement of email spam detection using improved deep learning algorithms for cyber security." Journal of Computer Security, August 2, 2021, 1–34. http://dx.doi.org/10.3233/jcs-200111.

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Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model i
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Unniram, Parambil Ajmal Roshan, Rohan Pokratath, Muhammed Jibin Parammal, et al. "Data for "Atomically precise surface chemistry of zirconium and hafnium metal oxo clusters beyond carboxylate ligands"." Chemical Science, June 5, 2024. https://doi.org/10.5281/zenodo.11484643.

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<strong>Data for Figures in the Publication: "Atomically Precise Surface Chemistry of Zirconium and Hafnium Metal Oxo Clusters Beyond Carboxylate Ligands"</strong> The provided .pxp files contain the experimental data corresponding to the figures presented in the manuscript. These files can be opened and edited using IGOR Pro version 8.0 or higher. Additionally, the .psd files contain image data and can be accessed or modified using Adobe Photoshop. <strong>Table of Contents:</strong> <strong>Scheme 1:</strong> (A) Schematic representation of a colloidally stable zirconium/hafnium oxide nanocr
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