C. Krishna Mohan

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In this paper, we investigate the problem of video classification into predefined genre. The approach adopted is based on spatial and temporal descriptors derived from short video sequences (20 seconds). By using support vector machines (SVMs), we propose an optimized multi-class classification method. Five popular TV broadcast genre namely cartoon,(More)
There are many clustering methods available and each of them may give a different grouping of datasets. It is proven that hybrid clustering algorithms give efficient results over the other algorithms. In this paper, we propose an efficient hybrid clustering algorithm by combining the features of leader's method which is an incremental clustering method and(More)
In this paper, a new method for detecting shot boundaries in video sequences using a late fusion technique is proposed. The method uses color histogram as the feature, and processes each bin separately for detecting shot boundaries. The decisions from individual bins are combined later for hypothesizing the presence of shot boundaries. The method provides a(More)
If subtransactions commute, the validity of con BLOCKINicts is reduced to the lifetime of subtransactions, i.e. to the execution of a single operation. Both advantages together signicantly increase the potential degree of concurrency on all levels of abstraction. The reduced con BLOCKINict rate makes the model applicable even for applications that require(More)
In this paper, we propose a method for classification of sport videos using edge-based features, namely edge direction histogram and edge intensity histo-gram. We demonstrate that these features provide discrimi-native information useful for classification of sport videos, by considering five sports categories, namely, cricket, football, tennis, basketball(More)
The field of music and speech classification is quite mature with researchers having settled on the approximate best discriminative representation. In this regard, Zubair et al. showed the use of sparse coefficients along with SVM to classify audio signals as music or speech to get a near-perfect classification. In the proposed method, we go one step(More)
In this paper, we proposed a method for classification of medical images captured by different sensors (modalities) based on multi-scale wavelet representation using dictionary learning. Wavelet features extracted from an image provide discrimination useful for classification of medical images, namely, diffusion tensor imaging (DTI), magnetic resonance(More)