Abstract
•The underlying originality of the proposed method relies on the application of the shadowed sets theory in image retrieval, which is a first involving shadowed sets in category of problems.•Techniques of automatic parameter selection are developed for automatically obtaining shadowed regions by combining saliency detection and edge detection.•A three-way division approach of images is presented to automatically split an image into three regions: salient regions, non-salient regions, and shadowed regions.•The salient regions and the shadowed regions are jointly used as the correctly detected regions to improve performance of segmentation and accuracy of retrieval.
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.