Learning shape detector by quantizing curve segments with multiple distance metrics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Representing and recognizing objects with massive local image patches
Pattern Recognition
Exploiting clustering approaches for image re-ranking
Journal of Visual Languages and Computing
Object categorization with sketch representation and generalized samples
Pattern Recognition
Robust stroke-based video animation via layered motion and correspondence
Proceedings of the 20th ACM international conference on Multimedia
Salient object detection based on regions
Multimedia Tools and Applications
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This paper presents a framework of layered graph matching for integrating graph partition and matching. The objective is to find an unknown number of corresponding graph structures in two images. We extract discriminative local primitives from both images and construct a candidacy graph whose vertices are matching candidates (i.e., a pair of primitives) and whose edges are either negative for mutual exclusion or positive for mutual consistence. Then we pose layered graph matching as a multicoloring problem on the candidacy graph and solve it using a composite cluster sampling algorithm. This algorithm assigns some vertices into a number of colors, each being a matched layer, and turns off all the remaining candidates. The algorithm iterates two steps: 1) Sampling the positive and negative edges probabilistically to form a composite cluster, which consists of a few mutually conflicting connected components (CCPs) in different colors and 2) assigning new colors to these CCPs with consistence and exclusion relations maintained, and the assignments are accepted by the Markov Chain Monte Carlo (MCMC) mechanism to preserve detailed balance. This framework demonstrates state-of-the-art performance on several applications, such as multi-object matching with large motion, shape matching and retrieval, and object localization in cluttered background.