2016/04/28 by Linwei Ye, Zhi Liu, Xiaofei Zhou +2
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Visual Attention and Saliency Detection
paper · doi:10.1109/lsp.2016.2558489
openalex publication_date 2016/04/28 · crossref created 2016/04/28 · crossref issued 2016/06/01 · crossref published 2016/06/01 · crossref published-print 2016/06/01 · crossref deposited 2022/01/12 · openalex created_date 2025/10/10 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/30
This letter proposes a novel saliency detection framework by propagating saliency of similar images retrieved from large and diverse Internet image collections to boost saliency detection performance effectively. For the input image, a group of similar images is retrieved based on the saliency weighted color histograms and the Gist descriptor from Internet image collections. Then, a pixel-level correspondence process between images is performed to guide the saliency propagation from the retrieved images. Both initial saliency map and correspondence saliency map are exploited to select the training samples by using the graph cut-based segmentation. Finally, the training samples are input into a set of weak classifiers to learn the boosted classifier for generating the boosted saliency map, which is integrated with the initial saliency map to generate the final saliency map. Experimental results on two public image datasets demonstrate that the proposed model can achieve the better saliency detection performance than the state-of-the-art single-image saliency models and co-saliency models.