Locality-Constrained Multi-Task Joint Sparse Representation for Image Classification

Lihua GUO  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.9   pp.2177-2181
Publication Date: 2013/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2177
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
image classification,  multi-task learning,  sparse representation,  manifold learning,  

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In the image classification applications, the test sample with multiple man-handcrafted descriptions can be sparsely represented by a few training subjects. Our paper is motivated by the success of multi-task joint sparse representation (MTJSR), and considers that the different modalities of features not only have the constraint of joint sparsity across different tasks, but also have the constraint of local manifold structure across different features. We introduce the constraint of local manifold structure into the MTJSR framework, and propose the Locality-constrained multi-task joint sparse representation method (LC-MTJSR). During the optimization of the formulated objective, the stochastic gradient descent method is used to guarantee fast convergence rate, which is essential for large-scale image categorization. Experiments on several challenging object classification datasets show that our proposed algorithm is better than the MTJSR, and is competitive with the state-of-the-art multiple kernel learning methods.