A classification-oriented dictionary learning model: Explicitly learning the particularity and commonality across categories |
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Affiliation: | 1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong;2. Automation Department, East China University of Science and Technology, Shanghai, China;1. Applied Math and Analisis Dept, University of Barcelona, Gran Via de les Corts Catalanes. 585, 08007 Barcelona, Spain;2. Computer Vision Center, Campus UAB, Edifici O, 08193 Bellaterra, Spain;3. Computer Science, Multimedia, and Telecommunications Dept, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain;1. Institute of Mathematics and Statistics, Universidade de São Paulo, Brazil;2. Escola Politécnica, Universidade de São Paulo, Brazil;1. Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi''an Jiaotong University, Xi''an 710049, PR China;2. Department of Geography & Resource Management, The Chinese University of Hong Kong, Hong Kong, PR China |
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Abstract: | Empirically, we find that despite the most exclusively discriminative features owned by one specific object category, the various classes of objects usually share some common patterns, which do not contribute to the discrimination of them. Concentrating on this observation and motivated by the success of dictionary learning (DL) framework, in this paper, we propose to explicitly learn a class-specific dictionary (called particularity) for each category that captures the most discriminative features of this category, and simultaneously learn a common pattern pool (called commonality), whose atoms are shared by all the categories and only contribute to representation of the data rather than discrimination. In this way, the particularity differentiates the categories while the commonality provides the essential reconstruction for the objects. Thus, we can simply adopt a reconstruction-based scheme for classification. By reviewing the existing DL-based classification methods, we can see that our approach simultaneously learns a classification-oriented dictionary and drives the sparse coefficients as discriminative as possible. In this way, the proposed method will achieve better classification performance. To evaluate our method, we extensively conduct experiments both on synthetic data and real-world benchmarks in comparison with the existing DL-based classification algorithms, and the experimental results demonstrate the effectiveness of our method. |
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Keywords: | Dictionary learning Sparse coding Image classification Particularity Commonality |
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