Efficient image retrieval approaches for different similarity requirements

The amount of pictorial data grows enormously with the expansion of the World Wide Web. From the large number of images, it is very important for users to retrieve desired images via an efficient and effective mechanism. In this paper we propose two efficient approaches to facilitate image retrieval by using a simple method to represent the image content. Each image is partitioned into mxn equal-sized sub-images (or blocks). A color that has enough number of pixels in a block is extracted to represent its content. In the first approach, the image content is represented by the extracted colors of the blocks. The spatial information of images is considered in image retrieval. In the second approach, the colors of the blocks in an image are used to extract objects (or regions). A block-level process is proposed to perform the region extraction. The spatial information of regions is considered unimportant in image retrieval. Our experiments show that these two block-based approaches can speed up the image retrieval. Moreover, the two approaches are effective for different requirements of image similarity. Users can choose a propel approach to process their queries based on their similarity requirements.