Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86924
Title: Exemplar based deep discriminative and shareable feature learning for scene image classification
Authors: Zuo, Zhen
Wang, Gang
Shuai, Bing
Zhao, Lifan
Yang, Qingxiong
Keywords: Information Sharing
Deep Feature Learning
Issue Date: 2015
Source: Zuo, Z., Wang, G., Shuai, B., Zhao, L., & Yang, Q. (2015). Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification. Pattern Recognition, 48(10), 3004-3015.
Series/Report no.: Pattern Recognition
Abstract: In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.
URI: https://hdl.handle.net/10356/86924
http://hdl.handle.net/10220/45212
ISSN: 0031-3203
DOI: http://dx.doi.org/10.1016/j.patcog.2015.02.003
Rights: © 2015 Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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