A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric invariance in computer vision
Geometric invariance in computer vision
Model-Based Recognition of 3D Objects from Single Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sports video categorizing method using camera motion parameters
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Automatic Sports Video Genre Classification using Pseudo-2D-HMM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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The paper proposes a novel approach for classification of sports images based on the geometric information encoded in the image of a sport's field. The proposed approach uses invariant nature of a crossratio under projective transformation to develop a robust classifier. For a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using Hough transform. These cross-ratios are represented by a histogram which forms a feature vector for the image. An SVM classifier trained on aprior model histograms of crossratios for sports fields is used to decide the most likely sport's field in the image. Experimental validation shows robust classification using the proposed approach for images of Tennis, Football, Badminton, Basketball taken from dissimilar view points.