Visual Perceptual Learning

Perceptual learning should be considered as an active process that embeds particular abstraction, reformulation and approximation within the abstraction framework. In this paper we focus on sparse coding theory and granular computing model for visual perceptual learning. We propose a novel sparse coding model, called here classification-oriented sparse coding (COSC) model for learning sparse and informative structures in natural images for visual classification task, combining the discriminability constraint supervised by visual classification task, besides the sparseness criteria. An attention-guided sparse coding model is also proposed in the paper. This model is a data-driven attention module based on the response saliency. For the granular computing based on tolerance relation we construct a more uniform granulation model, which is established on both consecutive space and discrete attribute space