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Patch-level spatial layout for classification and weakly supervised localization
Abstract : We propose a discriminative patch-level spatial layout model suitable for training with weak supervision. We start from a block-sparse model of patch appearance based on the normalized Fisher vector representation. The appearance model is responsible for i) selecting a discriminative subset of visual words, and ii) identifying distinctive patches assigned to the selected subset. These patches are further filtered by a sparse spatial model operating on a novel representation of pairwise patch layout. We have evaluated the proposed pipeline in image classification and weakly supervised localization experiments on a public traffic sign dataset. The results show significant advantage of the proposed spatial model over state of the art appearance models.
Cited literature [38 references]
https://hal.inria.fr/hal-01186677
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Submitted on : Thursday, August 27, 2015 - 3:12:25 PM
Last modification on : Thursday, January 20, 2022 - 5:30:16 PM
Long-term archiving on: : Saturday, November 28, 2015 - 10:19:52 AM
Contributor : Thoth Team Connect in order to contact the contributor
Submitted on : Thursday, August 27, 2015 - 3:12:25 PM
Last modification on : Thursday, January 20, 2022 - 5:30:16 PM
Long-term archiving on: : Saturday, November 28, 2015 - 10:19:52 AM
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