Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning
This work addresses the challenge of integrating diverse image descriptors for computer vision tasks, offering an incremental improvement over existing sparse coding techniques.
The paper tackles the problem of fusing multiple image descriptors for improved visual recognition by proposing a sparse coding and dictionary learning method in a multiple kernel space, achieving superior performance in object recognition and image clustering compared to existing sparse coding approaches and competitive results with other state-of-the-art methods.
In complex visual recognition tasks it is typical to adopt multiple descriptors, that describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1-D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.