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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>\n            It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point (\n            <jats:italic>e.g.,\u00a0<\/jats:italic>\n            a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.\n          <\/jats:p>","DOI":"10.1145\/3397188","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T10:06:57Z","timestamp":1592302017000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Sparse Graph Connectivity for Image Segmentation"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-0578","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Jilian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, China"}]},{"given":"Yonggang","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, Guilin, China"}]},{"given":"Guangquan","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangxi Normal University, Guilin, China"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"320","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1998.710790"},{"key":"e_1_2_1_5_1","volume-title":"Watershed-based segmentation and region merging. 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