Category-specific scene categorization

Abstract

Scene categorization with category-specific visual-word construction and image representation is proposed in this study. The proposed scene categorization has effective discriminative ability and class extendibility. The reasons are listed as follows. First, since the visual-word construction and image representation are category-specific, the corresponding learning model for classification has substantial discriminating power. Second, since the visual-word construction and image representation are category-specific, image features related to the original classes need not be recreated when new classes are added, which minimizes reconstruction overhead. Experimental results confirm that the accuracy of the proposed method is superior to existing methods with single-type features both in single-scale and in multi-scale versions.