SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor

Xiaochun Cao, Hua Zhang, Si Liu, Xiaojie Guo, Liang Lin; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 313-320


Recently, studies on sketch, such as sketch retrieval and sketch classification, have received more attention in the computer vision community. One of its most fundamental and essential problems is how to more effectively describe a sketch image. Many existing descriptors, such as shape context, have achieved great success. In this paper, we propose a new descriptor, namely Symmetric-aware Flip Invariant Sketch Histogram (SYM-FISH) to refine the shape context feature. Its extraction process includes three steps. First the Flip Invariant Sketch Histogram (FISH) descriptor is extracted on the input image, which is a flip-invariant version of the shape context feature. Then we explore the symmetry character of the image by calculating the kurtosis coefficient. Finally, the SYM-FISH is generated by constructing a symmetry table. The new SYM-FISH descriptor supplements the original shape context by encoding the symmetric information, which is a pervasive characteristic of natural scene and objects. We evaluate the efficacy of the novel descriptor in two applications, i.e., sketch retrieval and sketch classification. Extensive experiments on three datasets well demonstrate the effectiveness and robustness of the proposed SYM-FISH descriptor.

Related Material

author = {Cao, Xiaochun and Zhang, Hua and Liu, Si and Guo, Xiaojie and Lin, Liang},
title = {SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}