{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:01:24Z","timestamp":1760241684585,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,11]],"date-time":"2018-07-11T00:00:00Z","timestamp":1531267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703115","61673125","61702251"],"award-info":[{"award-number":["61703115","61673125","61702251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Frontier and Key Technology Innovation Special Funds of Guangdong Province","award":["2016B090910003","2014B090919002"],"award-info":[{"award-number":["2016B090910003","2014B090919002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>We propose an incremental spectral clustering method for stream data clustering and apply it to stream image segmentation. The main idea in our work consists of generating the data points in the kernel space by Fastfood features and iteratively calculating the eigendecomposition of data. Compared with the popular Nystr\u00f6m-based approximation, our work accesses each data point only once while Nystr\u00f6m, in particular the sampling scheme, will go through the entire dataset first and calculate the embeddings of data points with a second visit. As a result, our method is able to learn data partitions incrementally and improve eigenvector approximation with more and more data seen from a stream. By contrast, the performance of the standard Nystr\u00f6m is fixed when the sample set is selected. Experimental results show the superiority of our method.<\/jats:p>","DOI":"10.3390\/sym10070272","type":"journal-article","created":{"date-parts":[[2018,7,11]],"date-time":"2018-07-11T10:50:09Z","timestamp":1531306209000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Incremental Spectral Clustering via Fastfood Features and Its Application to Stream Image Segmentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0261-4068","authenticated-orcid":false,"given":"Li","family":"He","sequence":"first","affiliation":[{"name":"Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuangbin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengcai","family":"Leng","sequence":"additional","affiliation":[{"name":"Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.patcog.2017.03.012","article-title":"Unsupervised hierarchical image segmentation through fuzzy entropy maximization","volume":"68","author":"Yin","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TIP.2015.2401516","article-title":"Integrated Foreground Segmentation and Boundary Matting for Live Videos","volume":"24","author":"Gong","year":"2015","journal-title":"IEEE Trans. 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