Ming-Hen Tsai

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Attribute-based query offers an intuitive way of image retrieval, in which users can describe the intended search targets with understandable attributes. In this paper, we develop a general and powerful framework to solve this problem by leveraging a large pool of weak attributes comprised of automatic classifier scores or other mid-level representations(More)
In this paper, we decompose the problem of active learning into two parts, learning with few examples and learning by querying labels of samples. The first part is achieved mainly by SVM classifiers. We also consider variants based on transductive learning. In the second part, based on SVM decision values, we propose a framework to flexibly select points(More)
In this paper, we improve the efficiency of kernelized support vector machine (SVM) for image classification using linearized kernel data representation. Inspired by Nyström approximation, we propose a decomposition technique for converting the kernel data matrix into an approximated primal form. This allows us to apply the approximated kernelized data in(More)
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