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A Survey on Content Based Image Retrieval Using Color, Texture and Shape Features
Abstract
Due to increase in volume of images in database, content based image retrieval becomes a challenging problem. To overcome such problems and efficient access of images from database, image retrieval uses low level features such as color, shape and texture that are prominent to retrieve the images. These features are extracted from the images. At last images are retrieved relevant to the query image from the database based on the similarity measurements. In this paper some low level features and their limitations are described. Furthermore, future scope is also suggested. Relevance feedback technique can be used to reduce the semantic gap between human perception and computerized system.
Keywords: CBIR, color histogram, color moments, Tamura texture feature, Hu moments, Zernike moments
Cite this Article
Vivek Verma, Punit Kumar Johari. A Survey on Content Based Image Retrieval Using Color, Texture and Shape Features. Journal of Operating Systems Development & Trends. 2015; 2(2): 1–8p.
References
Brahmi D, Ziou D. Improving CBIR Systems by Integrating Semantic Features. In Proc. RIAO, Vaucluse, France. Apr 2004; 291–305p.
Kim WY, Kim YS. A Region Based Shape Descriptor using Zernike Moments. Signal Processing: Image Communication. 2000; 16: 95–102p.
Jadhav Shital S, Swati Patil. Content Based Image Retrieval Using Color and Texture Feature with Efficient Relevance Feedback. IJARCSSE. Oct 2014; 4(10).
Zhihu Huang, Jinsong Leng. Analysis of Hu’s Moment Invariants on Image Scaling and Rotation. IEEE Trans. Comput. 2010.
Ye Mei, Dimitrios Androutsos. Robust Affine Invariant Region-Based Shape Descriptor and the Whitening Zernike Moment Shape Descriptor. IEEE Signal Processing Lett. Oct 2009; 16(10).
Sagarmay Deb. Using Relevance Feedback in Bridging Semantic Gaps in Content-based Image Retrieval. International Conference on Advances in Future. 2010.
Lei Li, Dongsheng Wang, Guohua Cui. Trademark Image Retrieval using Region Zernike Moments. International Symposium on Intelligent Information Technology Application. 2008.
Juothi B, Madhavee Latha Y, Reddy VSK. Relevance Feed Back Content Based Image Retrieval Using Multiple Features. IEEE Trans. Comput. 2010.
Chia-Hung Wei, Yue Li, Wing-Yin Chau, et al. Trademark Image Retrieval Using Synthetic Features for Describing Global Shape and Interior Structure. Pattern Recogn. 2009; 42: 386–394p.
Ya-Li Qi. A Trademark Retrieval method based on Support Vector Machines Self-learning. International Forum on Computer Science-Technology and Applications. 2009.
Xiongxin Hu. The Invariant of Zernike Moment in Image Recognition about Intravenous Infusion Needle. IEEE Trans. Comput. 2010.
Puviarasan N, Bhavani R, Vasanthi A. Image Retrieval using Combination of Texture and Shape Features. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE). Mar 2014; 3(3).
Deepti Agrawal, Anand Singh Jalal, Rajesh Tripathi. Trademark Image Retrieval by Integrating Shape with Texture Feature. IEEE. 2013.
Usha R, Perumal K. Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification. International Journal of Computer Science and Communication Networks. 4(5): 169–174p.
Mori Tamura S, Yamawaki T. Textural Features Corresponding to Visual Perception. IEEE Trans. Systems, Man Cybern. Jun 1978; smc-8(6): 460–473p.
Gaikwad Abhay N, Dharmendra Singh, Nigam MJ. Recognition of Target in through Wall Imaging Using Shape Feature Extraction. IEEE Trans. Comput. 2011.
Seunggyu Kim, Seongdo Kim, Youngjung Uh, et al. Color and Shape Feature-based Detection of Speed Sign in Real-time. IEEE International Conference on Systems, Man and Cybernetics. Oct 2012.
Cai-kou Chen, Qiang-qiang-qiang Sun, Jing-yu Yang. Binary Trademark Image Retrieval Using Region Orientation Information Entropy. IEEE International Conference on Computational Intelligence and Security Workshops. 2007.
Herman J, Sheeba Rani J, Devaraj D. Face Recognition using Generalized Pseudo-Zernike Moment. IEEE Trans. Comput. 2009.
Mohd. Danish, Ritika Rawat, Ratika Sharma. A Survey: Content Based Image Retrieval Based on Color, Texture, Shape and Neuro Fuzzy. IJERA. Sep–Oct 2013; 3(5): 839–844p.
Nishant Shrivastava, Vipin Tyagi. Content based Image Retrieval Based on Relative Locations of Multiple Regions of Interest Using Selective Regions Matching. Information Science. 2013.
Yangxi Li, Chao Zhou, Bo Geng, et al. A Comprehensive Study on Learning to Rank for Content-Based Image Retrieval. Signal Processing. 2013; 93: 1426–1434p.
Sudipta Mukhopadhyay, Jatindra Kumar Dash, Rahul Das Gupta. Content-Based Texture Image Retrieval Using Fuzzy Class Membership. Pattern Recogn. Lett. 2013; 34: 646–654p.
Deying Feng, Jie Yang, Congxin Liu. An Efficient Indexing Method for Content-Based Image Retrieval. Neurocomputing. 2013; 106: 103–114p.
Bae-Muu Chang, Hung-Hsu Tsai, Wen-Ling Chou. Using Visual Features to Design a Content-Based Image Retrieval Method Optimized by Particle Swarm Optimization Algorithm. Eng. Appl. Artif. Intell. 2013.
Ahmed Talib, Massudi Mahmuddin, Husniza Husni, et al. A Weighted Dominant Color Descriptor for Content-Based Image Retrieval. J. Vis. Commun. Image R. 2013; 24: 345–360p.
Romain Raveaux, Jean-Christophe Burie, Jean-Marc Ogier. Structured Representations in a Content Based Image Retrieval Context. J. Vis. Commun. Image R. 2013; 24: 1252–1268p.
Fazal Malik, Baharum Baharudin. Analysis of Distance Metrics in Content-Based Image Retrieval using Statistical Quantized Histogram Texture Features in the DCT Domain. Computer and Information Sciences. 2013; 25: 207–218p.
Karthikeyan M, Aruna P. Probability Based Document Clustering and Image Clustering using Content-Based Image Retrieval. Appl. Soft Comput. 2013; 13: 959–966p.
Nitin Jain, Salankar SS. Color and Texture Feature Extraction for Content Based Image Retrieval. IOSR-JEEE. 2014; 53–58p.
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