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This paper studies the problem of end-to-end windows mining directly from detection output. Traditional object detection systems approach this problem in an ad-hoc manner, say, Non-Maximum Suppression (NMS). Beyond NMS, multi-class context modeling has been explored thoroughly recent years. But all these methods put their emphasis on eliminating false positive windows rather than improving recall...
The increasing number and resolution of earth observation (EO) imaging sensors has had a significant impact on both the acquired image data volume and the information content in images. There is consequently a strong need for highly efficient search tools for EO image databases and for search methods to automatically identify and recognize structures within EO images. In this paper, we present a concept...
In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision...
Recently, in the fields of internet and social networking, the classification and filtering of naked images has been receiving a significant amount of attention. In this paper, we propose a novel naked image classification which can make effective use of semantic features of a naked image. In addition, a novel measurement, termed accumulated distance ratio (ADR), is proposed in order to systematically...
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