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Author Zhuang, Liansheng ♦ She, Lanbo ♦ Jiang, Yuning ♦ Tang, Ketan ♦ Yu, Nenghai
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description In: Proceedings of the Fifth International Conference on Image and Graphics
In this paper, we propose Semi-Supervised pLSA(SS-pLSA) for image classification. Compared with the clas-sic non-supervised pLSA, our method overcomes the short-coming of poor classification performance when the fea-tures of two categories are quite similar. By introducing category label information into EM algorithm, the itera-tion process can be directed carefully to the desired result. SS-pLSA greatly prevents the inter-impact between different categories. The experiment results show that the proposed SS-pLSA significantly improves the performance of image classification, especially when two categories ’ features are similar and difficult to distinguish by classic pLSA. In con-trast to these totally supervised algorithm, SS-pLSA almost has no loss in detection rate while sharply reduces the diffi-culty of collecting training samples. With highly flexibility, SS-pLSA enables users to explore the trade-off between la-beled number and accuracy. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2009-01-01