Sketch-Based Retrieval Using Content-Aware Hashing

In this paper, we introduce a generic hashing-based approach. It aims to facilitate sketch-based retrieval on large datasets of visual shapes. Unlike previous methods where visual descriptors are extracted from overlapping grids, a content-aware selection scheme is proposed to generate candidate patches instead. Meanwhile, the saliency of each patch is efficiently estimated. Locality-sensitive hashing LSH is employed to integrate and capture both the content and saliency of patches, as well as the spatial information of visual shapes. Furthermore, hash codes are indexed so that a query can be processed in sub-linear time. Experiments on three standard datasets in terms of hand drawn shapes, images and 3D models demonstrate the superiority of our approach.