Ming-Hen Tsai

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• MARR relies only on the pre-labeled attributes to design the dependency model. • User labeling is a burdensome process. The small amount of attributes are far from sufficient in forming an expressive feature space. • Weak Attributes are a collection of mid-level representations, which could be comprised of automatic classifier scores, distances to certain(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)
Searching images based on descriptions of image attributes is an intuitive process that can be easily understood by humans and recently made feasible by a few promising works in both the computer vision and multimedia communities [4,7,9,11]. In this report, we describe some experiments of image retrieval methods that utilize weak attributes [11].
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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