Sketch-based Image Retrieval via Shape Words

Abstract

The explosive growth of touch screens has provided a good platform for sketch-based image retrieval. However, most previous works focused on low level descriptors of shapes and sketches. In this paper, we try to step forward and propose to leverage shape words descriptor for sketch-based image retrieval. First, the shape words are defined and an efficient algorithm is designed for shape words extraction. Then we generalize the classic Chamfer Matching algorithm to address the shape words matching problem. Finally, a novel inverted index structure is proposed to make shape words representation scalable to large scale image databases. Experimental results show that our method achieves competitive accuracy but requires much less memory, e.g., less than 3% of memory storage of MindFinder. Due to its competitive accuracy and low memory cost, our method can scale up to much larger database.