Sketch-based Image Retrieval via Shape Words

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 de ned and an
e cient algorithm is designed for shape words extraction.
Then we generalize the classic Chamfer Matching algorith-
m 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 databas-
es. 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.