In image retrieval, global features related to color or texture are commonly used to describe the image content. The use of interest points in content-based image retrieval allows image index to represent local properties of images. In this paper, we present a wavelet-based salient point extraction algorithm. We show that extracting the color and texture information in the locations given by these points provides significantly improved results in terms of retrieval accuracy, computational complexity, and storage space of feature vectors as compared to the global feature approaches.
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