Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Sports video categorizing method using camera motion parameters
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Horror film genre typing and scene labeling via audio analysis
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Shot Type Classification in Sports Video Based on Visual Attention
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
| Hi-index | 0.00 |
We propose a novel approach classify different sports videos given their groups. First, the SURF descriptors in each key frames are extracted. Then they are used to form the visual word vocabulary (codebook) by using K-Means clustering algorithm. After that, the histogram of these visual words are computed and considered as a feature vector. Finally, we use SVM to train each classifier for each category. The classification result of the video is the production of the scores output from all of the key frames. An extensive experiment is performed on a diverse and challenging dataset of 600 sports video clips downloaded from Youtube with a total of more than 6000 minutes in length for 10 different kinds of sports.