Content-Based Image Retrieval using Scale Invariant Feature Transform and moments

The rapid growth of different types of images has posed a great challenge for scientific fraternity across the world. For easy access to large number of images, efficient indexing and retrieval is required. The field of Content-Based Image Retrieval (CBIR) attempts to solve this problem. This paper proposes a combination of local and global features for CBIR. Local features are extracted through Scale Invariant Feature Transform (SIFT) and global features are extracted through geometric moments. The final feature vector is constructed by combining local and global features which is used to retrieve visually similar images. The proposed method is tested on Corel-1K dataset and its performance is measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods in terms of precision.