Multi-Constraints-Based Enhanced Class-Specific Dictionary Learning for Image Classification
Sparse representation based on dictionary learning has been widely applied in recognition tasks. These methods only work well under the conditions that the training samples are uncontaminated or contaminated by a little noise. However, with increasing noise, these methods are not robust for image classification. To address the problem, we propose a novel multi-constraints-based enhanced class-specific dictionary learning (MECDL) approach for image classification, of which our dictionary learning framework is composed of shared dictionary and class-specific dictionaries. For the class-specific dictionaries, we apply Fisher discriminant criterion on them to get structured dictionary. And the sparse coefficients corresponding to the class-specific dictionaries are also introduced into Fisher-based idea, which could obtain discriminative coefficients. At the same time, we apply low-rank constraint into these dictionaries to remove the large noise. For the shared dictionary, we impose a low-rank constraint on it and the corresponding intra-class coefficients are encouraged to be as similar as possible. The experimental results on three well-known databases suggest that the proposed method could enhance discriminative ability of dictionary compared with state-of-art dictionary learning algorithms. Moreover, with the largest noise, our approach both achieves a high recognition rate of over 80%.
KeywordsSparse representation Dictionary learning Low-rank matrix recovery Discriminative coefficients Image classification
This work was supported by the National Nature Science Foundation of China (61876087, 61272222), and the Key Program of the National Natural Science Foundation of China (61432008).
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