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Relevant bibliographies by topics / Color Correlogram

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Journal articles
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Academic literature on the topic 'Color Correlogram'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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Journal articles on the topic "Color Correlogram"

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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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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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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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Dissertations / Theses on the topic "Color Correlogram"

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Zhao, Qi. "Color correlogram representation and differential earth mover's distance matching /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2009. http://uclibs.org/PID/11984.

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Zhuang, Yuwen. "Metric Based Automatic Event Segmentation and Network Properties Of Experience Graphs." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337372416.

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Veľas, Martin. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236399.

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This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with e

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Book chapters on the topic "Color Correlogram"

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Lei, Jingsheng. "Image Annotation Using Sub-block Energy of Color Correlograms." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05253-8_61.

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B., Swapna, Arulmozhi P., Kamalahasan M., et al. "A Secure and Effective Image Retrieval Based on Robust Features." In Advances in Social Networking and Online Communities. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9640-1.ch005.

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The most typical approaches are content-based image retrieval systems. Content-based picture retrieval may be the only one in all the image retrieval techniques that uses user visual options of an image like color, form, and texture. The objective is to retrieve the set of pictures quickly and economically by supported color and texture options. Color is the foremost authoritative and utilized visual option that is invariant to image dimension and adjustment. Color car correlogram includes the special correlation and figures the mean color of all components of intensity about a distance k-th o

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Conference papers on the topic "Color Correlogram"

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Birinci, Murat, Serkan Kiranyaz, and Moncef Gabbouj. "Image color content description utilizing perceptual color correlogram." In 2008 International Workshop on Content-Based Multimedia Indexing. IEEE, 2008. http://dx.doi.org/10.1109/cbmi.2008.4564947.

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Park, Jongan, Youngeun An, Ilhoe Jeong, Gwangwon Kang, and Kim Pankoo. "Image Indexing using Spatial Multi-Resolution Color Correlogram." In 2007 IEEE International Workshop on Imaging Systems and Techniques. IEEE, 2007. http://dx.doi.org/10.1109/ist.2007.379604.

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Park, Jongan, Seungjo Han, and Youngeun An. "Heuristic Features for Color Correlogram for Image Retrieval." In 2008 International Conference on Computational Science and Its Applications (ICCSA). IEEE, 2008. http://dx.doi.org/10.1109/iccsa.2008.50.

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Zhang, Tao, Shu-Min Fei, Hong Lu, and Xiao-Dong Li. "Color Correlogram Based Particle Filter for Object Tracking." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.45.

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Xu, Dongbin, Lei Huang, and Changping Liu. "Object Tracking Using Particle Filter Based on Color Correlogram." In 2008 Second International Symposium on Intelligent Information Technology Application (IITA). IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.345.

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Fierro-Radilla, Atoany, Karina Perez-Daniel, Mariko Nakano-Miyatake, and Jenny Benois. "Dominant color correlogram descriptor for content-based image retrieval." In Sixth International Conference on Graphic and Image Processing (ICGIP 2014), edited by Yulin Wang, Xudong Jiang, and David Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2179020.

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Maheen, Jubeena B., and R. P. Aneesh. "Machine Learning Algorithm for Fire Detection using Color Correlogram." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993246.

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Lei Hu, Shuqiang Jiang, Qingming Huang, and Wen Gao. "People re-detection using Adaboost with sift and color correlogram." In 2008 15th IEEE International Conference on Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4712013.

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Deepak, C. R., R. Umesh Babu, Khande Bharath Kumar, and C. M. Rahul Krishnan. "Shot boundary detection using color correlogram and Gauge-SURF descriptors." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726510.

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Rasheed, Waqas, Youngeun An, Sungbum Pan, Ilhoe Jeong, Jongan Park, and Jinsuk Kang. "Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram." In 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008). IEEE, 2008. http://dx.doi.org/10.1109/mue.2008.27.

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