Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139870
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dc.contributor.authorChen, Taoen_US
dc.contributor.authorLu, Shijianen_US
dc.contributor.authorFan, Jiayuanen_US
dc.date.accessioned2020-05-22T05:48:09Z-
dc.date.available2020-05-22T05:48:09Z-
dc.date.issued2017-
dc.identifier.citationChen, T., Lu, S., & Fan, J. (2018). S-CNN : subcategory-aware convolutional networks for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2522-2528. doi:10.1109/TPAMI.2017.2756936en_US
dc.identifier.issn0162-8828en_US
dc.identifier.urihttps://hdl.handle.net/10356/139870-
dc.description.abstractThe marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.rights© 2017 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleS-CNN : subcategory-aware convolutional networks for object detectionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TPAMI.2017.2756936-
dc.identifier.pmid28961103-
dc.identifier.scopus2-s2.0-85030759381-
dc.identifier.issue10en_US
dc.identifier.volume40en_US
dc.identifier.spage2522en_US
dc.identifier.epage2528en_US
dc.subject.keywordsSubcategoryen_US
dc.subject.keywordsObject Detectionen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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