Discriminative Block-Diagonal Representation Learning for Image Recognition

Abstract

Existing block-diagonal representation researches mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semi-supervised framework of low-rank representation. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intra-class representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image datasets, three character datasets, and the fifteen scene multi-categories dataset. It not only shows superior potential on image recognition but also outperforms state-of-the-art methods.

Accepted by TNNLS, and the matlab codes are available at https://sites.google.com/site/darrenzz219/Home/publication

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Cited by in corpus (11)

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• Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
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• I-WKNN: Fast-Speed and High-Accuracy WIFI Positioning for Intelligent Stadiums
• Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
• Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
• Low Rank Regularization: A Review
• Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
• Non-negative representation based discriminative dictionary learning for face recognition
• Learning a Representation with the Block-Diagonal Structure for Pattern Classification