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This observation\ninspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed\nalgorithm provides a mathematically simple and computationally efficient method for the unsupervised\nlearning of image components, even in conditions where these elementary features overlap\nconsiderably. To test the proposed algorithm, a novel artificial task is introduced which is similar\nto the frequently\u2010used bars problem but employs squares rather than bars to increase the degree of\noverlap between components. Using this task, we investigate how the proposed method performs on\nthe parsing of artificial images composed of overlapping features, given the correct representation\nof the individual components; and secondly, we investigate how well it can learn the elementary\ncomponents from artificial training images. We compare the performance of the proposed algorithm\nwith its predecessors including variations on these algorithms that have produced state\u2010of\u2010the\u2010art\nperformance on the bars problem. The proposed algorithm is more successful than its predecessors\nin dealing with overlap and occlusion in the artificial task that has been used to assess performance.<\/jats:p>","DOI":"10.1155\/2009\/381457","type":"journal-article","created":{"date-parts":[[2009,5,5]],"date-time":"2009-05-05T13:52:02Z","timestamp":1241531522000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Unsupervised Learning of Overlapping Image Components   Using Divisive Input Modulation"],"prefix":"10.1155","volume":"2009","author":[{"given":"M. W.","family":"Spratling","sequence":"first","affiliation":[]},{"given":"K.","family":"De Meyer","sequence":"additional","affiliation":[]},{"given":"R.","family":"Kompass","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2009,5,5]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"crossref","unstructured":"FengT. LiS. Z. ShumH.-Y. andZhangH. Local non-negative matrix factorization as a visual representation Proceedings of the 2nd International Conference on Development and Learning (ICDL \u203202) June 2002 Cambridge Mass USA 178\u2013186 https:\/\/doi.org\/10.1109\/DEVLRN.2002.1011835.","DOI":"10.1109\/DEVLRN.2002.1011835"},{"key":"e_1_2_7_2_2","doi-asserted-by":"crossref","unstructured":"HoyerP. O. Non-negative sparse coding Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing (NNSP \u203202) September 2002 Martigny Switzerland 557\u2013565 https:\/\/doi.org\/10.1109\/NNSP.2002.1030067.","DOI":"10.1109\/NNSP.2002.1030067"},{"key":"e_1_2_7_3_2","first-page":"1457","article-title":"Non-negative matrix factorization with sparseness constraints","volume":"5","author":"Hoyer P. O.","year":"2004","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/44565"},{"key":"e_1_2_7_5_2","doi-asserted-by":"crossref","unstructured":"LiS. Z. HouX. W. ZhangH. J. andChengQ. S. 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