{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:05:07Z","timestamp":1761663907123,"version":"3.41.0"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T00:00:00Z","timestamp":1555027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2017JBZ108"],"award-info":[{"award-number":["2017JBZ108"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2019,5,31]]},"abstract":"<jats:p>Recently, co-saliency detection, which aims to automatically discover common and salient objects appeared in several relevant images, has attracted increased interest in the computer vision community. In this article, we present a novel graph-matching based model for co-saliency detection in image pairs. A solution of graph matching is proposed to integrate the visual appearance, saliency coherence, and spatial structural continuity for detecting co-saliency collaboratively. Since the saliency and the visual similarity have been seamlessly integrated, such a joint inference schema is able to produce more accurate and reliable results. More concretely, the proposed model first computes the intra-saliency for each image by aggregating multiple saliency cues. The common and salient regions across multiple images are thus discovered via a graph matching procedure. Then, a graph reconstruction scheme is proposed to refine the intra-saliency iteratively. Compared to existing co-saliency detection methods that only utilize visual appearance cues, our proposed model can effectively exploit both visual appearance and structure information to better guide co-saliency detection. Extensive experiments on several challenging image pair databases demonstrate that our model outperforms state-of-the-art baselines significantly.<\/jats:p>","DOI":"10.1145\/3313874","type":"journal-article","created":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T12:07:04Z","timestamp":1555330024000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Co-saliency Detection with Graph Matching"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6100-2788","authenticated-orcid":false,"given":"Zun","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]},{"given":"Congyan","family":"Lang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]},{"given":"Jiashi","family":"Feng","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Yidong","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]},{"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,4,12]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"4175","article-title":"Self-adaptively weighted co-saliency detection via rank constraint","volume":"23","author":"Cao Xiaochun","year":"2014","journal-title":"IEEE Trans. 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