{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T04:03:48Z","timestamp":1750133028557,"version":"3.41.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319541808"},{"type":"electronic","value":"9783319541815"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-54181-5_9","type":"book-chapter","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T15:27:37Z","timestamp":1489073257000},"page":"137-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Saliency Detection via Diversity-Induced Multi-view Matrix Decomposition"],"prefix":"10.1007","author":[{"given":"Xiaoli","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixiang","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiujun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Baciu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,10]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Zuo, W., Meng, D., Zhang, L., Feng, X., Zhang, D.: A generalized iterated shrinkage algorithm for non-convex sparse coding. In: ICCV, pp. 217\u2013224 (2013)","key":"9_CR1","DOI":"10.1109\/ICCV.2013.34"},{"doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155\u20131162 (2013)","key":"9_CR2","DOI":"10.1109\/CVPR.2013.153"},{"doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166\u20133173 (2013)","key":"9_CR3","DOI":"10.1109\/CVPR.2013.407"},{"doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., Hornung, A.: Saliency filters: Contrast based filtering for salient region detection. In: CVPR, pp. 733\u2013740 (2012)","key":"9_CR4","DOI":"10.1109\/CVPR.2012.6247743"},{"doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: BMVC (2011)","key":"9_CR5","DOI":"10.5244\/C.25.110"},{"doi-asserted-by":"crossref","unstructured":"Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR, pp. 853\u2013860 (2012)","key":"9_CR6","DOI":"10.1109\/CVPR.2012.6247758"},{"doi-asserted-by":"crossref","unstructured":"Cheng, M.-M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.-M.: Global contrast based salient region detection. In: CVPR, pp. 409\u2013416 (2011)","key":"9_CR7","DOI":"10.1109\/CVPR.2011.5995344"},{"doi-asserted-by":"crossref","unstructured":"Chang, K.-Y., Liu, T.-L., Chen, H.-T., Lai, S.-H.: Fusing generic objectness and visual saliency for salient object detection. In: ICCV, pp. 914\u2013921 (2011)","key":"9_CR8","DOI":"10.1109\/ICCV.2011.6126333"},{"doi-asserted-by":"crossref","unstructured":"Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR, pp. 2814\u20132821 (2014)","key":"9_CR9","DOI":"10.1109\/CVPR.2014.360"},{"unstructured":"Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: ICML, pp. 663\u2013670 (2010)","key":"9_CR10"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1109\/TIP.2011.2169274","volume":"21","author":"C Lang","year":"2012","unstructured":"Lang, C., Liu, G., Yu, J., Yan, S.: Saliency detection by multitask sparsity pursuit. IEEE Trans. Image Process. 21, 1327\u20131338 (2012)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Zou, W., Kpalma, K., Liu, Z., Ronsin, J.: Segmentation driven low-rank matrix recovery for saliency detection. In: BMVC (2013)","key":"9_CR12","DOI":"10.5244\/C.27.78"},{"key":"9_CR13","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/1970392.1970395","volume":"58","author":"E Cand\u00e8s","year":"2011","unstructured":"Cand\u00e8s, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58, 11\u201320 (2011)","journal-title":"J. ACM"},{"unstructured":"Lin, Z., Chen, M.-M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arxiv:1009.5055 (2010)","key":"9_CR14"},{"doi-asserted-by":"crossref","unstructured":"Peng, H., Li, B., Ji, R., Hu, W., Xiong, W., Lang, C.: Salient object detection via low-rank and structured sparse matrix decomposition. In: AAAI, pp. 796\u2013802 (2013)","key":"9_CR15","DOI":"10.1609\/aaai.v27i1.8591"},{"unstructured":"Simoncelli, E., Freeman, W.: The steerable pyramid: A flexible architecture for multi-scale derivative computation. In: ICIP (1995)","key":"9_CR16"},{"key":"9_CR17","volume-title":"Gabor Analysis and Algorithms: Theory and Applications","author":"HG Feichtinger","year":"2012","unstructured":"Feichtinger, H.G., Strohmer, T.: Gabor Analysis and Algorithms: Theory and Applications. Birkh\u00e4user, Basel (2012)"},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"12","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 12, 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"P Felzenszwalb","year":"2004","unstructured":"Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59, 167\u2013181 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"9_CR20","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1109\/83.913596","volume":"10","author":"M Cetin","year":"2001","unstructured":"Cetin, M., Karl, W.C.: Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Trans. Image Process. 10, 623\u2013631 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR21","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/LSP.2007.898300","volume":"14","author":"R Chartrand","year":"2007","unstructured":"Chartrand, R.: Exact reconstruction of sparse signals via nonconvex minimization. IEEE Sig. Process. Lett. 14, 707\u2013710 (2007)","journal-title":"IEEE Sig. Process. Lett."},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1088\/0266-5611\/24\/3\/035020","volume":"24","author":"R Chartrand","year":"2008","unstructured":"Chartrand, R., Staneva, V.: Restricted isometry properties and nonconvex compressive sensing. Inverse Prob. 24, 657\u2013682 (2008)","journal-title":"Inverse Prob."},{"doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, C., Fu, H., Liu, S., Zhang, H.: Diversity-induced multi-view subspace clustering. In: CVPR, pp. 586\u2013594 (2015)","key":"9_CR23","DOI":"10.1109\/CVPR.2015.7298657"},{"doi-asserted-by":"crossref","unstructured":"Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.J.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. (2016). In Press","key":"9_CR24","DOI":"10.1109\/TPAMI.2016.2562626"},{"doi-asserted-by":"crossref","unstructured":"Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML, pp. 129\u2013136 (2009)","key":"9_CR25","DOI":"10.1145\/1553374.1553391"},{"doi-asserted-by":"crossref","unstructured":"Tang, W., Lu, Z., Dhillon, I.S.: Clustering with multiple graphs. In: ICDM, pp. 1016\u20131021 (2009)","key":"9_CR26","DOI":"10.1109\/ICDM.2009.125"},{"key":"9_CR27","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548\u20131560 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Tong, N., Lu, H., Ruan, X., Yang, M.-H.: Salient object detection via bootstrap learning. In: CVPR, pp. 1884\u20131892 (2015)","key":"9_CR28","DOI":"10.1109\/CVPR.2015.7298798"},{"doi-asserted-by":"crossref","unstructured":"Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.-H.: Saliency detection via dense and sparse reconstruction. In: ICCV, pp. 2976\u20132983 (2013)","key":"9_CR29","DOI":"10.1109\/ICCV.2013.370"},{"key":"9_CR30","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.patcog.2015.12.014","volume":"54","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Sun, X., Xu, C., Baciu, G.: Multiple feature distinctions based saliency flow model. Pattern Recogn. 54, 190\u2013205 (2016)","journal-title":"Pattern Recogn."},{"key":"9_CR31","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2015.12.009","volume":"182","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Xu, C., Sun, X., Baciu, G.: Schatten-q regularizer constrained low rank subspace clustering model. Neurocomputing 182, 36\u201347 (2016)","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"He, Z., Sun, X., Zhang, X., Xu, C.: Saliency detection via nonconvex regularization based matrix decomposition. In: International Conference on Computational Intelligence and Security, pp. 243\u2013247 (2015)","key":"9_CR32","DOI":"10.1109\/CIS.2015.67"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2016"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-54181-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T14:22:10Z","timestamp":1750083730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-54181-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319541808","9783319541815"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-54181-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"10 March 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.accv2016.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}