Fusion of Local and Global Features using Stationary Wavelet Transform for Efficient Content Based Image Retrieval

In this paper we propose a hybrid approach for Content Based Image Retrieval that takes into account both global as well as local features of an image. Towards this, first Stationary Wavelet Transform is applied on query image to extract horizontal, vertical and diagonal detail matrices. Stationary Wavelet Transform is used because of its translational invariant property. After this global textural features are extracted using Gray level Co-occurrence Matrix for each of these sub-matrices. To aid the retrieval process, a local descriptor is also computed by splitting the image into sub-regions. Finally Euclidean distance is used to retrieve the relevant results. Experimental results show that the proposed approach provides significant improvement over existing methods.