Discriminative sparse neighbor coding
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Sparse coding has received extensive attention in the literature of image classification. Traditional sparse coding strategies tend to approximate local features in terms of a linear combination of basis vectors, without considering feature neighboring relationships. In this scenario, similar instances in the feature space may result in totally different sparse codes. To address this shortcoming, we investigate how to develop new sparse representations which preserve feature similarities. We commence by establishing two modules to improve the discriminative ability of sparse representation. The first module selects discriminative features for each class, and the second module eliminates non-informative visual words. We then explore the distribution of similar features over the dominant basis vectors for each class. We incorporate the feature distribution into the objective function, spanning a class-specific low dimensional subspace for effective sparse coding. Extensive experiments on various image classification tasks validate that the proposed approach consistently outperforms several state-of-the-art methods.
Multimedia Tools and Applications
© 2015 Springer Science+Business Media New York. This is an electronic version of an article published in Multimedia Tools and Applications, Volume 75, Issue 7, pp 4013–4037, 2016. Multimedia Tools and Applications is available online at: http://link.springer.com/ with the open URL of your article.