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In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set...
In this paper, we describe the use of a Boosting algorithm, Real AdaBoost, for content-based image retrieval (CBIR) on a large number (190) of keyword categories. Previous work with Boosting for image orientation detection has involved only a few categories, such as a simple outdoor vs. indoor scene dichotomy. Other
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
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