Abstract
In content based image retrieval, relevance feedback has been extensively studied to bridge the gap between low level image features and high level semantic concepts. However, it is still challenged by small sample size problem, since users are usually not so patient to label a large number of training instances. In this paper, two strategies are proposed to tackle this problem: (1) a novel active selection criterion. It takes into consideration both the informative and the representative measures. With this criterion, the diversities of the selected images are increased while their informative powers are kept, thus more information gain can be obtained from the feedback images; and (2) incorporation of unlabeled images within the co-training framework. Unlabeled data partially alleviates the training data scarcity problem, thus can improve the efficiency of SVM active learning. Systematic experimental results verify the superiority of our method over some existing active learning methods.
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Liu, R., Wang, Y., Baba, T., Uehara, Y., Masumoto, D., Nagata, S. (2007). SVM-Based Active Feedback in Image Retrieval Using Clustering and Unlabeled Data. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_118
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DOI: https://doi.org/10.1007/978-3-540-74272-2_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
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