Content Based Image Retrieval and Classification using speeded-up robust features (SURF) and grouped bag-of-visual-words (GBoVW)

This paper presents a work in progress for a proposed method for Content Based Image Retrieval (CBIR) and Classification. The proposed method makes use of the interest points detector and descriptor called Speeded-Up Robust Features (SURF) combined with Bag-of-Visual-Words (BoVW). The combination yields a good retrieval and classification result when compared to other methods. Moreover, a new dictionary building method in which each group has its own dictionary is also proposed. Our method is tested on the highly diverse COREL1000 database and has shown a more discriminative classification and retrieval result.