Abstract
Object-based image retrieval has been an active research topic in recent years, in which a user is only interested in some object in the images. The recently proposed methods try to comprehensively use both image- and region-level features for more satisfactory performance, but they either cannot well explore the relationship between the two kinds of features or lead to heavy computational load. In this paper, by adopting support vector machine (SVM) as the basic classifier, a novel multi-instance learning method is proposed. To deal with the different forms of image- and region-level representations, standard SVM and multi-instance SVM are utilized respectively. Moreover, the relationship between images and their segmented regions is also taken into account. A unified optimization framework is developed to involve all the available information, and an efficient iterative solution is introduced. Experimental results on the benchmark data set demonstrate the effectiveness of our proposal.
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Li, F., Liu, R., Baba, T. (2013). Multi-SVM Multi-instance Learning for Object-Based Image Retrieval. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_4
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DOI: https://doi.org/10.1007/978-3-642-40261-6_4
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