Kernel Methods are algorithms that are widely used, mainly because they can implicitly perform a non-linear mapping of the input data to a high dimensional feature space. In this paper, novel Kernel Matrices, that reflect the general structure of data, are proposed for classification. The proposed Matrices exploit properties of the graph theory, which are generated using power iterations of already known Kernel Matrices and three approaches are presented. Experiments on various datasets are conducted and statistical tests are performed, comparing our proposed approach against current Kernel Matrices used on support vector machines. Also, experiments on real datasets for folk dance and activity recognition that highlight the superiority of our proposed method, are provided.
In this paper, we propose a novel graph-based kernel method in order to construct histograms for a bag of words approach, by using similarity measures, applied in activity recognition problems. Bag of words is the most popular framework for performing classification on video data. This framework, however, is an orderless collection of features. We propose a better way to encode action in videos, via altering the histograms. The creation of such histograms is performed based on kernel methods, inspired from graph theory, computed with no great additional computational cost. Moreover, when using the proposed algorithm to construct the histograms, a richer representation of videos is attained. Experiments on folk dances recognition were conducted based on our proposed method, by comparing histograms extracted from a typical bag-of-words framework against histograms of the proposed method, which provided promising results on this challenging task.
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