Collaborative Clustering Approach Based on Dempster-Shafer Theory for Bag-of-Visual-Words Codebook Generation

Abstract

Feature encoding methods play an important role in the performance of the recognition tasks. The Bag-of-Visual-Words (BoVW) paradigm aims to assign the feature vectors to the codebook visual words. However, in the codebook generation phase, different clustering algorithms can be used, each giving a different set of visual words. Thus, the choice of the discriminative visual words set is a challenging task. In this work, we propose an enhanced bag-of-visual-words codebook generation approach using a collaborative clustering method based on the Dempster-Shafer Theory (DST). First, we built three codebooks using the k-means, the Fuzzy C-Means (FCM), and the Gaussian Mixture Model (GMM) clustering algorithms. Then, we computed the Agreement Degrees Vector (ADV) between the clusters of the pairs (k-means, GMM) and (k-means, FCM). We merged the obtained ADVs using the DST in order to generate the clusters weights. We evaluated the proposed approach for Remote Sensing Image Scene Classification (RSISC). The results proved the effectiveness of our proposed approach and showed that it can be applied for different recognition tasks in various domains.