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COLOR AND TEXTURE FEATURES IN CONTENT-BASED IMAGE RETRIEVAL: A COMPREHENSIVE SURVEY

Amit Meena , Assistant Professor, Department of Computer Science and Engineering, Modern Institute of Technology & Research Center, Rajasthan, India

Abstract

Content-Based Image Retrieval (CBIR) plays a crucial role in efficiently searching and retrieving images from vast databases. This comprehensive survey investigates the application of color and texture features in CBIR systems. We explore the evolution of CBIR technology, delve into various color and texture feature extraction techniques, and examine their impact on retrieval performance. The study provides valuable insights into the state of the art in CBIR, offering a foundation for researchers and practitioners seeking to enhance image retrieval using color and texture features.

Keywords

Content-Based Image Retrieval (CBIR), Color features, Texture features

References

N. Ganar, C. S. Jambhulkar, S. M. Gode, “Enhancement of image retrieval by using color, texture and shape features,” in Int. Conf. on Electron. System, Signal Process. and Computing Technologies, 2013.

M. Danish, R. Rawat and R. Sharma, “A survey: Content based image retrieval based on color, texture, shape and neuro fuzzy,” Int. J. of Eng. Res. and Appl., vol. 3, no. 5, pp. 839-844, 2013. [Online]. Available: https://pdfs.semanticscholar.org/c2f6/67d7f31e04dd03aa56ef4273dbf07390c2c4.pdf

P. Shaktawat and V. K. Govindan, "Novel scheme for image retrieval using combination of color-texture,” Int. J. of Comput. Trends and Technol., vol. 21, no. 2, pp. 98-102, 2015. [Online]. Available: http://www.ijcttJ..org/2015/Volume21/number-2/IJCTT-V21P118.pdf

Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra, “Relevance feedback: a power tool for interactive content-based image retrieval,” IEEE Transcation on Circuits and Syst. for Video Technol., vol. 8, no. 5, pp. 644-655, 1998. doi: 10.1109/76.718510

H. A. Jalab, “Image retrieval system based on color layout descriptor and Gabor filters," in 2011 IEEE Conf. on Open Systems, Langkawi, Malaysia, 2011. doi: 10.1109/ICOS.2011.6079266

M. Fakheri, M. C. Amirani and T. Sedghi, “Gabor wavelets and GVF functions for feature extraction in efficient content based color and texture images retrieval,” in 2011 7th Iranian Conf. on Mach. Vision and Image Process., Teheran, Iran. doi: 10.1109/IranianMVIP.2011.6121598

Z. Huang, P. P. K. Chan, W. W. Y. Ng and D. S. Yeung, “Content based image retrieval using color moment and Gabor texture feature,” in 2010 Int. Conf. on Mach. Learning and Cybern., 2010. doi: 10.1109/ICMLC.2010.5580566

M. Rakhee, V. K. Govindan and B. Karun, “Enhancing the precision of walsh wavelet based approach for color and texture feature extraction in CBIR by including a shape feature,” Cybern. and Inform. Technologies, vol. 13, no. 2, pp. 97-106, 2013. doi: 10.2478/cait-2013-0018

R.Thakkar and O. Kale, “Get high precision in content based image retrieval using combination of color, texture and shape features,” Int. J. of Eng. Develop. and Res., vol. 2, no. 2, 2014. [Online]. Available: https://www.ijedr.org/viewfull.php?&p_id=IJEDR1402113

K. S. Arun and V. K. Govindan, “Optimizing visual dictionaries for effective image retrieval,” Int. J. of Multimedia Inform. Retrieval, vol. 4, no. 3, pp. 165-185, 2015. doi: https://doi.org/10.1007/s13735-015-0076-1

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How to Cite

Amit Meena. (2021). COLOR AND TEXTURE FEATURES IN CONTENT-BASED IMAGE RETRIEVAL: A COMPREHENSIVE SURVEY. International Journal of Computer Science & Information System, 6(01), 01–05. Retrieved from https://scientiamreearch.org/index.php/ijcsis/article/view/61