Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
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Signal Processing
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Proceedings of the ACM International Conference on Image and Video Retrieval
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IEEE Transactions on Image Processing
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Computers and Electrical Engineering
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ITNG '10 Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations
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Computer Standards & Interfaces
Image Retrieval System Based on Adaptive Color Histogram and Texture Features
The Computer Journal
IEEE Transactions on Image Processing
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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.