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Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using...
We propose a novel semi-supervised boosting algorithm using linear programming, which explicitly maximizes the margin over both labeled and unlabeled data. Experiments conducted on a number of UCI datasets and synthetic data show that, the algorithm we propose performs better than the state-of-the-art supervised and semi-supervised boosting algorithms, and it is more robust with noisy data.
Web contents are going overwhelming today. The numerous online documents, webpages, e-books, etc. are much useful but obtaining them is also time-consuming. Text categorization is one of the solutions to the issue. For all text categorization method, Support Vector Machines (SVM) is one of the most acceptable one. However, to perform more efficiently on webpages, it is necessary to add improvements...
In this paper we present a new method for object categorization. Firstly an image representation is obtained by the proposed hierarchical learning method consisting of alternating between local coding and maximum pooling operations, where the local coding operation induces discrimination while the image descriptor and maximum pooling operation induces invariance in hierarchical architecture. Then...
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