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We extract multilevel hand-crafted features associated with locality-constrained linear coding and convolutional neural network features as image occupation descriptors. To avoid the curse of dimensionality and overfitting, a boost strategy called multichannel SVM is used to integrate features from face and body. Intra- and interclass visual variations are jointly considered in the boosting framework to further improve performance. In the evaluation, we verify the effectiveness of predicting occupation from face and demonstrate promising performance obtained by combining face and body information. More importantly, our work further integrates deep features into the multichannel SVM framework and shows significantly better performance over the state of the art.<\/jats:p>","DOI":"10.1145\/3009911","type":"journal-article","created":{"date-parts":[[2016,12,2]],"date-time":"2016-12-02T19:13:45Z","timestamp":1480706025000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Occupation from Images by Combining Face and Body Context Information"],"prefix":"10.1145","volume":"13","author":[{"given":"Wei-Ta","family":"Chu","sequence":"first","affiliation":[{"name":"National Chung Cheng University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Hao","family":"Chiu","sequence":"additional","affiliation":[{"name":"National Chung Cheng University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,2]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Predicting elections: Child\u2019s play&excl","author":"Antonakis John","year":"2009","unstructured":"John Antonakis and Olaf Dalgas . 2009. Predicting elections: Child\u2019s play&excl ; Science 323, 5918 ( 2009 ), 1183. John Antonakis and Olaf Dalgas. 2009. 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