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As a derivative of Restricted Boltzmann Machine (RBM), classification RBM (Class RBM) is proved to be an effective classifier with a probabilistic interpretation. Several elegant learning methods/models related to Class RBM have been proposed. This paper proposes and analyzes a Rényi divergence based generalization for discriminative learning objective of Class RBM. Specifically, we extend the Conditional...
We propose to use action, scene and object concepts as semantic attributes for classification of video events in InTheWild content, such as YouTube videos. We model events using a variety of complementary semantic attribute features developed in a semantic concept space. Our contribution is to systematically demonstrate the advantages of this concept-based event representation (CBER) in applications...
Low-level appearance as well as spatio-temporal features, appropriately quantized and aggregated into Bag-of-Words (BoW) descriptors, have been shown to be effective in many detection and recognition tasks. However, their effcacy for complex event recognition in unconstrained videos have not been systematically evaluated. In this paper, we use the NIST TRECVID Multimedia Event Detection (MED11 [1])...
In order to solve the problem that algorithm SVM (Support Vector Machine) is very slowly for intrusion detection systems, a novel algorithm based on SVM divided up by clusters was proposed. In the method, Training set is divided into many subsets by clustering algorithm, and these subsets are classified by the decision function SVM. Detection Experiments with the algorithm on intrusion detection data...
We present a system that improves accuracy of food intake assessment using computer vision techniques. Traditional dietetic method suffers from the drawback of either inaccurate assessment or complex lab measurement. Our solution is to use a mobile phone to capture images of foods, recognize food types, estimate their respective volumes and finally return quantitative nutrition information. Automated...
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