TY - GEN
T1 - An incremental structured part model for image classification
AU - Zhang, Huigang
AU - Bai, Xiao
AU - Cheng, Jian
AU - Zhou, Jun
AU - Zhao, Huijie
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Image classification
KW - incremental learning
KW - semantic parts
KW - structural relationship
UR - https://www.scopus.com/pages/publications/84868107940
U2 - 10.1007/978-3-642-34166-3_53
DO - 10.1007/978-3-642-34166-3_53
M3 - 会议稿件
AN - SCOPUS:84868107940
SN - 9783642341656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 483
EP - 491
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2012, Proceedings
T2 - Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012
Y2 - 7 November 2012 through 9 November 2012
ER -