SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.

TitleSEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.
Publication TypeJournal Article
Year of Publication2012
AuthorsWang, B, Xiong, H, Jiang, X, Ling, F
JournalProc Int Conf Image Proc
Pagination2157-2160
Date Published2012
ISSN1522-4880
iDASH CategoryImaging Informatics
AbstractObject recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called "structure kernel", which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels.
Alternate JournalProc Int Conf Image Proc
PubMed ID23666108