Abstract
Eliminating semantic gaps is important for image retrieving and annotating in content based image retrieval (CBIR), especially under web context. In this paper, a relevance feedback learning approach is proposed for web image retrieval, by using soft support vector machine (Soft-SVM). An active learning process is introduced to Soft-SVM based on a novel sampling rule. The algorithm extends the conventional SVM by using a loose factor to make the decision plane partial to the uncertain data and reduce the learning risk. To minimize the overall cost, a new feedback model and an acceleration scheme are applied to the learning system for reducing the cost of data collection and improving the classifier accuracy. The algorithm can improve the performance of image retrieving effectively.
This work is partially supported by the National Basic Research Program of China (2006CB303103) and the National Natural Science Foundation of China (60573090).
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Zhang, Y., Wang, D., Yu, G. (2008). Relevance Feedback Learning for Web Image Retrieval Using Soft Support Vector Machine. In: Ishikawa, Y., et al. Advanced Web and Network Technologies, and Applications. APWeb 2008. Lecture Notes in Computer Science, vol 4977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89376-9_20
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DOI: https://doi.org/10.1007/978-3-540-89376-9_20
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