Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework
Kui Wu
Kim-Hui Yap
Nanyang Technol. Univ., Singapore;
This paper appears in: Computational Intelligence Magazine, IEEE
Publication Date: May 2006
Volume: 1,
Issue: 2
On page(s): 10-16
ISSN: 1556-603X
INSPEC Accession Number: 8909182
Digital Object Identifier: 10.1109/MCI.2006.1626490
Current Version Published: 2006-05-01
Abstract
Conventional relevance feedback in content-based image retrieval (CBIR) systems uses only the labeled images for learning. Image labeling, however, is a time-consuming task and users are often unwilling to label too many images during the feedback process. This gives rise to the small sample problem where learning from a small number of training samples restricts the retrieval performance. To address this problem, we propose a technique based on the concept of pseudo-labeling in order to enlarge the training data set. As the name implies, a pseudo-labeled image is an image not labeled explicitly by the users, but estimated using a fuzzy rule. Therefore, it contains a certain degree of uncertainty or fuzziness in its class information. Fuzzy support vector machine (FSVM), an extended version of SVM, takes into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework called pseudo-label fuzzy support vector machine (PLFSVM) to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method
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