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In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-016-1318-9","type":"journal-article","created":{"date-parts":[[2016,11,16]],"date-time":"2016-11-16T14:51:52Z","timestamp":1479307912000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors"],"prefix":"10.1186","volume":"17","author":[{"given":"Yang","family":"Song","sequence":"first","affiliation":[]},{"given":"Weidong","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Dagan","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mei","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,11,16]]},"reference":[{"issue":"17","key":"1318_CR1","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1093\/bioinformatics\/btn346","volume":"24","author":"H Peng","year":"2008","unstructured":"Peng H. 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