Showing papers by "C. Krishna Mohan published in 2010"

Classification of sport videos using edge-based features and autoassociative neural network models

Abstract: In this paper, we propose a method for classification of sport videos using edge-based features, namely edge direction histogram and edge intensity histogram. We demonstrate that these features provide discriminative information useful for classification of sport videos, by considering five sports categories, namely, cricket, football, tennis, basketball and volleyball. The ability of autoassociative neural network (AANN) models to capture the distribution of feature vectors is exploited, to develop class-specific models using edge-based features. We show that combining evidence from complementary edge features results in improved classification performance. Also, combination of evidence from different classifiers like AANN, hidden Markov model (HMM) and support vector machine (SVM) helps improve the classification performance. Finally, the performance of the classification system is examined for test videos which do not belong to any of the above five categories. A low rate of misclassification error for these test videos validates the effectiveness of edge-based features and AANN models for video classification.

Efficient clustering approach using incremental and hierarchical clustering methods

Abstract: 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 complete linkage algorithm which is a hierarchical clustering procedure. It is most common to find the dissimilarity between two clusters as the distance between their centorids or the distance between two closest (or farthest) data points. However, these measures may not give efficient clustering results in all cases. So, we propose a new similarity measure, known as cohesion to find the intercluster distance. By using this measure of cohesion, a two level clustering algorithm is proposed, which runs in linear time to the size of input data set. We demonstrate the effectiveness of the clustering procedure by using the leader's algorithm and cohesion similarity measure. The proposed method works in two steps: In the first step, the features of incremental and hierarchical clustering methods are combined to partition the input data set into several smaller subclusters. In the second step, subclusters are merged continuously based on cohesion similarity measure. We demonstrate the effectiveness of this framework for the web mining applications.