Attention-driven salient edge(s) and region(s) extraction with application to CBIR

Selective visual attention plays an important role for humans to understand an image by intuitively emphasizing some salient parts. Such mechanism can be well applied in localized content-based image retrieval, due to the fact that in the context of CBIR, the user is only interested in a portion of the image and the rest of the image is irrelevant. Being aware of this, in this paper, the selective visual attention model (SVAM) is incorporated in the CBIR task to estimate the user's retrieval concept. In contrast with existing learning based retrieval algorithms which need relevance feedback strategy to get user's high-level semantic information, the proposed method does not need any user's interaction to provide the training data. From this point of view, our method can be regarded as the purely bottom-up manner while learning based algorithms belong to the top-down manner. Specifically, an improved saliency map computing algorithm is employed first. Then, based on the saliency map, an efficient salient edges and regions detection method is introduced. Moreover, the concepts of salient edge histogram descriptors (SEHDs) and salient region adjacency graphs (SRAGs) are proposed, respectively, for images' similarity comparison. Finally, an integrated strategy is adopted for content-based image retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfying retrieval performance.