Understanding Image Structure via Hierarchical Shape Parsing

Xian-Ming Liu, Rongrong Ji, Changhu Wang, Wei Liu, Bineng Zhong, Thomas S. Huang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5042-5050

Abstract


Exploring image structure is a long-standing yet important research subject in the computer vision community. In this paper, we focus on understanding image structure inspired by the "simple-to-complex" biological evidence. A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space. To improve the robustness and flexibility of image representation, we further bundle the image appearances into hierarchical parsing trees. Image descriptions are subsequently constructed by performing a structural pooling, facilitating efficient matching between the parsing trees. We leverage the proposed hierarchical shape parsing to study two exemplar applications including edge scale refinement and unsupervised "objectness" detection. We show competitive parsing performance comparing to the state-of-the-arts in above scenarios with far less proposals, which thus demonstrates the advantage of the proposed parsing scheme.

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[bibtex]
@InProceedings{Liu_2015_CVPR,
author = {Liu, Xian-Ming and Ji, Rongrong and Wang, Changhu and Liu, Wei and Zhong, Bineng and Huang, Thomas S.},
title = {Understanding Image Structure via Hierarchical Shape Parsing},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}