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Content-based image retrieval by integrating color and texture features

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Abstract

Content-based image retrieval (CBIR) has been an active research topic in the last decade. Feature extraction and representation is one of the most important issues in the CBIR. In this paper, we propose a content-based image retrieval method based on an efficient integration of color and texture features. As its color features, pseudo-Zernike chromaticity distribution moments in opponent chromaticity space are used. As its texture features, rotation-invariant and scale-invariant image descriptor in steerable pyramid domain are adopted, which offers an efficient and flexible approximation of early processing in the human visual system. The integration of color and texture information provides a robust feature set for color image retrieval. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for different test DBs.

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Correspondence to Xiang-Yang Wang.

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This work was supported by the National Natural Science Foundation of China under Grant No. 60773031 & 60873222, the Open Foundation of State Key Laboratory of Information Security of China under Grant No. 04-06-1, the Open Foundation of Network and Data Security Key Laboratory of Sichuan Province, the Open Foundation of Key Laboratory of Modern Acoustics Nanjing University under Grant No. 08–02, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. 2008351 & L2010230.

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Wang, XY., Zhang, BB. & Yang, HY. Content-based image retrieval by integrating color and texture features. Multimed Tools Appl 68, 545–569 (2014). https://doi.org/10.1007/s11042-012-1055-7

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