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Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize...
Context information has been widely studied for recognizing collective activities. Most existing works assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities can be coexisted and serve as the context for each other in real-world scenarios. Based on this observation, we propose a novel approach to model both the intra-class and inter-class...
In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pair wise image matching scheme to select the discriminative features. Only the label information from the raining-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the...
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