An incremental structured part model for image classification

Authors:
Huigang Zhang;Xiao Bai;Jian Cheng;Jun Zhou;Huijie Zhao
Affiliations:
School of Computer Science and Engineering, Beihang University, Beijing, China;School of Computer Science and Engineering, Beihang University, Beijing, China;Institute of Automation Chinese Academy of Sciences, Beijing, China;School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia;School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

The state-of-the-art image classification methods usually require many training samples to achieve good performance. To tackle this problem, we present a novel incremental method in this paper, which learns a part model to classify objects using only a small number of training samples. Our model captures the inherent connections of the semantic parts of objects and builds structural relationship between them. In the incremental learning stage, we use high entropy images that have been accepted by users to update the learned model. The proposed approach is evaluated on two datasets, which demonstrates its advantages over several alternative classification methods in the literature.