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2012 International Conference on Computational Intelligence and Software Engineering (CiSE 2012)(E-BOOK)

Wuhan,China,2012-12-142012-12-162012

ISBN: 978-1-61896-036-8 Scientific Research Publishing

E-Book 275pp Pub. Date: December 2012

Category: Computer Science & Communications

Price: $100

Title: Semi-Supervised SVM Learning Algorithm for image Retrieval
Source: 2012 International Conference on Computational Intelligence and Software Engineering (CiSE 2012)(E-BOOK) (pp 159-162)
Author(s): Changsheng Zhou, Computer Center, Beijing Information and Science and Technology University,Beijing, China
Guizhi Li, Computer Center, Beijing Information and Science and Technology University,Beijing, China
Abstract: Relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used in content-based image retrieval to bridge the semantic gap between low-level visual features and high-level human perception. However, the Conventional SVM based RF uses only the labeled images for learning which gives rise to the small sample problem. When the training data is insufficient, the performance of SVM may drop dramatically. In this paper, we proposed a method to alleviate the small sample problem in SVM based RF by using semisupervised learning algorithm which uses a large amount of unlabeled data together with labeled data to build better models. The proposed semi-supervised approach employs hierarchical clustering to label the unlabeled data which can be used to train the SVM classification. The use of unlabeled data can improve the efficiency of SVM active learning. We compared our method with standard active SVM based RF on a database of 10,000 images, the experiment results show that our method has a better performance and prove that it is an effective algorithm for CBIR.
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