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Soft-Labeling Image Scheme Using Fuzzy Support Vector Machine

  • Kui Wu
  • Kim-Hui Yap
Part of the Studies in Computational Intelligence book series (SCI, volume 96)

In relevance feedback of content-based image retrieval (CBIR) systems, the number of training samples is usually small since image labeling is a time consuming task and users are often unwilling to label too many images during the feedback process. This results in the small sample problem where the performance of relevance feedback is constrained by the small number of training samples. In view of this, we propose a soft-labeling technique that investigates the use of unlabeled data in order to enlarge the training data set. The contribution of this book chapter is the development of a soft labeling framework that strives to address the small sample problem in CBIR systems. By studying the characteristics of labeled images, we propose to utilize an unsupervised clustering algorithm to select unlabeled images, which we call soft-labeled images. The relevance of the soft-labeled images is estimated using a fuzzy membership function, and integrated into the fuzzy support vector machine (FSVM) for effective learning. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method.

Keywords

Support Vector Machine Image Retrieval Relevance Feedback Color Histogram Unlabeled Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kui Wu
    • 1
  • Kim-Hui Yap
    • 1
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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