Browsing by Author "Yegnanarayana, B."

Now showing 1 - 4 of 4

Edge-based sports video classification using HMM
(2007) Krishna, Mohan, C.; Yegnanarayana, B.
In this paper, we propose a method for sports videos genre classification using edge-based feature, namely edge direction histogram and edge intensity histogram. We demonstrate that these features provide discriminative information useful for sports video classification. We use hidden Markov models (HMMs) to model the sports video categories. Evidence from the two edge based features are combined using a linear weighting rule. We also show that combining evidence from complementary edge features results in improved classification performance. We demonstrate the application of this framework to five sport genre types, namely, cricket, football, tennis, basketball and volleyball. Copyright � 2007 IICAI.
Edge-based sports video classification using HMM
(2007) Krishna Mohan, C.K.; Yegnanarayana, B.
In this paper, we propose a method for sports videos genre classification using edge-based feature, namely edge direction histogram and edge intensity histogram. We demonstrate that these features provide discriminative information useful for sports video classification. We use hidden Markov models (HMMs) to model the sports video categories. Evidence from the two edge based features are combined using a linear weighting rule. We also show that combining evidence from complementary edge features results in improved classification performance. We demonstrate the application of this framework to five sport genre types, namely, cricket, football, tennis, basketball and volleyball. Copyright Â© 2007 IICAI.
Video shot segmentation using late fusion technique
(2008) Mohan, C.K.; Dhananjaya, N.; Yegnanarayana, B.
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 certain degree of robustness against illumination and camera/object motion, as it ignores small changes in the bins. While the early fusion techniques rely on the extent of change in color information, the proposed technique relies on the number of significant changes. Experimental results successfully validate the new method and show that it can effectively detect both abrupt and gradual transitions. � 2008 IEEE.
Video shot segmentation using late fusion technique
(2008) Krishna Mohan, C.K.; Dhananjaya, N.; Yegnanarayana, B.
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 certain degree of robustness against illumination and camera/object motion, as it ignores small changes in the bins. While the early fusion techniques rely on the extent of change in color information, the proposed technique relies on the number of significant changes. Experimental results successfully validate the new method and show that it can effectively detect both abrupt and gradual transitions. Â© 2008 IEEE.