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Learning Discriminative and Shareable Features for Scene Classification
Zhen Zuo1, Gang Wang1, 2, Bing Shuai1, Lifan Zhao1, Qingxiong Yang3, and Xudong Jiang1
1Nanyang Technological University, Singapore
2Advanced Digital Sciences Center, Sinapore
3City University of Hong Kong, China
Abstract. In this paper, we propose to learn a discriminative and shareable feature transformation filter bank to transform local image patches (represented as raw pixel values) into features for scene image classification. The learned filters are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative and class-specific information. For each category, a subset of the filters are activated in a data-adaptive manner, meanwhile sharing of filters among different categories is also allowed. Discriminative power of the filter bank is further enhanced by enforcing the features from the same category to be close to each other in the feature space, while features from different categories to be far away from each other. The experimental results on three challenging scene image classification datasets indicate that our features can achieve very promising performance. Furthermore, our features also show great complementary effect to the state-of-the-art ConvNets feature.
Keywords: Feature learning, Discriminant analysis, Information sharing, Scene Classificsion
LNCS 8689, p. 552 ff.
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© Springer International Publishing Switzerland 2014