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By assuming that a region is difficult to be reconstructed will become conspicuous, the authors represent the prior knowledge of a subject by a dictionary of sparse bases pre-trained on random images and estimate the conspicuity score of a region according to the activation costs of sparse bases as well as the sparse reconstruction error. Finally, the saliency map of an image is generated by summing up all conspicuity maps obtained. Experimental results show proposed approach achieves impressive performance in comparisons with 16 state-of-the-art approaches.<\/jats:p>","DOI":"10.4018\/ijmdem.2018040101","type":"journal-article","created":{"date-parts":[[2018,3,5]],"date-time":"2018-03-05T11:57:32Z","timestamp":1520251052000},"page":"1-20","source":"Crossref","is-referenced-by-count":0,"title":["A Randomized Framework for Estimating Image Saliency Through Sparse Signal Reconstruction"],"prefix":"10.4018","volume":"9","author":[{"given":"Kui","family":"Fu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJMDEM.2018040101-0","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247706"},{"key":"IJMDEM.2018040101-1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247711"},{"key":"IJMDEM.2018040101-2","unstructured":"Bruce, N., & Tsotsos, J. 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