We apply a biologically inspired model of visual object recognition to the multiclass object categorization problem. Our model modifies that of Serre, Wolf, and Poggio. As in that...
We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first a...
— We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an -class problem, which finds eigenvectors on which the projected class data are loca...
We introduce an object recognition system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to b...
Abstract. In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class ...