High discriminative SIFT feature and feature pair selection to improve the bag of visual words model

The bag of visual words (BOW) model has been widely applied in the field of image recognition and image classification. However, all scale-invariant feature transform (SIFT) features are clustered to construct the visual words which result in a substantial loss of discriminative power for the visual words. The corresponding visual phrases will further render the generated BOW histogram sparse. In this study, the authors aim to improve the classification accuracy by extracting high discriminative SIFT features and feature pairs. First, high discriminative SIFT features are extracted with the within- and between-class correlation coefficients. Second, the high discriminative SIFT feature pairs are selected by using minimum spanning tree and its total cost. Next, high discriminative SIFT features and feature pairs are exploited to construct the visual word dictionary and visual phrase dictionary, respectively, which are concatenated to a joint histogram with different weights. Compared with the state-of-the-art BOW-based methods, the experimental results on Caltech 101 dataset show that the proposed method has higher classification accuracy.