Improved adaptive image retrieval with the use of shadowed sets

Image retrieval algorithms based on the whole image exhibit high complexity due to background interference, low-level description abilities and large storage requirements, while image retrieval algorithms based on the saliency detection have been found to have low accuracy owing to the lack of important information in extracted salient regions caused by the uncertainty of the salient regions of the image. In this paper, we propose a shadowed-set-based image retrieval algorithm, and develop techniques of an automatic selection of two threshold parameters by combining saliency detection and edge detection, which automatically determine shadowed regions. The developed algorithm uses shadowed set theory to divide the image into salient regions, non-salient regions and shadowed regions, in order to extract the useful information of the image and ignore irrelevant one. As a consequence, this leads to the salient regions and the shadowed regions to be jointly involved in the retrieval process. The experimental results reported for several datasets show that the proposed algorithm can effectively improve the retrieval accuracy compared with the existing state-of-the-art algorithms.