{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:34:11Z","timestamp":1772861651559,"version":"3.50.1"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2017,7,27]],"date-time":"2017-07-27T00:00:00Z","timestamp":1501113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2014M560507"],"award-info":[{"award-number":["2014M560507"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61403116 and 61432019"],"award-info":[{"award-number":["61403116 and 61432019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>Saliency detection has recently received increasing research interest on using high-dimensional datasets beyond two-dimensional images. Despite the many available capturing devices and algorithms, there still exists a wide spectrum of challenges that need to be addressed to achieve accurate saliency detection. Inspired by the success of the light-field technique, in this article, we propose a new computational scheme to detect salient regions by integrating multiple visual cues from light-field images. First, saliency prior maps are generated from several light-field features based on superpixel-level intra-cue distinctiveness, such as color, depth, and flow inherited from different focal planes and multiple viewpoints. Then, we introduce the location prior to enhance the saliency maps. These maps will finally be merged into a single map using a random-search-based weighting strategy. Besides, we refine the object details by employing a two-stage saliency refinement to obtain the final saliency map.<\/jats:p>\n          <jats:p>\n            In addition, we present a more challenging benchmark dataset for light-field saliency analysis, named\n            <jats:italic>HFUT-Lytro<\/jats:italic>\n            , which consists of 255 light fields with a range from 53 to 64 images generated from each light-field image, therein spanning multiple occurrences of saliency detection challenges such as occlusions, cluttered background, and appearance changes. Experimental results show that our approach can achieve 0.6--6.7% relative improvements over state-of-the-art methods in terms of the\n            <jats:italic>F-measure<\/jats:italic>\n            and\n            <jats:italic>Precision<\/jats:italic>\n            metrics, which demonstrates the effectiveness of the proposed approach.\n          <\/jats:p>","DOI":"10.1145\/3107956","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T12:19:47Z","timestamp":1501244387000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":88,"title":["Saliency Detection on Light Field"],"prefix":"10.1145","volume":"13","author":[{"given":"Jun","family":"Zhang","sequence":"first","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3094-7735","authenticated-orcid":false,"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Lin","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Yang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Gao","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Rui","sequence":"additional","affiliation":[{"name":"Lenovo, Haidian, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,7,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_2_1_3_1","first-page":"3","article-title":"The plenoptic function and the elements of early vision","volume":"1","author":"Adelson Edward H.","year":"1991","journal-title":"Comput. 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