Hybrid feature-based wallpaper visual search

In this paper we propose a hybrid feature-based wallpaper visual search system. As opposed to conventional techniques that use global features to perform wallpaper search, this paper proposes to integrate local and global features to support both functions of recognition (identify the product ID of the query images) and retrieval (search wallpapers that are visually similar to the query images). An adaptive SIFT is designed to extract sufficient number of local features from both the query and reference images. The combination of the sparse and dense SIFT features results in a significant improvement of the recognition rate. Global features are further incorporated in the system for the visually similar image retrieval. A new query expansion is proposed to alleviate the problems caused by cluttered background, occlusion, scale change and illumination changes. Experiments on a dataset consisting of 2,208 reference images from 218 different designs show that the proposed method can achieve a recognition rate of more than 90%.