科研之友
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摘要
Music annotation Is an Important research topic in lite multimedia area. One of the challenges in music annotation is how to reduce the human effort in labeling music files for building reliable classification models. In the past there have been many studies on applying support vector machine active learning methods to automatic multimedia data annotation. which try to select the most informative examples for manually Most of these studies focused on selecting a single unlabeled example in. each iteration process for binary classification. As a result, the model has to be retrained after each labeled example is solicited, and the user is likely to lose patience after a few rounds of labeling In this paper. we present a novel multi-class active learning algorithm that can select multiple music examples for labeling m each iteration process. The key of the multi-sample selection for multi-class active learning is how to reduce the redundancy and avoid selecting lite outliers among the selected exampled such that each example provides unique information for model updating To this end, we propose the distance diversity and set density in the support vector machine feature space as lite measurement of the scatter of the selected sample set Experimental results on two music data sets demonstrate the effectiveness of our method Moreover, although our criterion is designed for music annotation. it can be used in a general frame work.
- 出版日期2010-3
- 单位华中科技大学
