Sports Video Classification Using Bag of Words Model

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 c...

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Bibliographic Details
Published inIntelligent Information and Database Systems pp. 316 - 325
Main Authors Duong, Dinh, Dinh, Thang Ba, Dinh, Tien, Duong, Duc
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2012
SeriesLecture Notes in Computer Science
Subjects
bag of words
image classification
K-means
sports video classification
SURF
SVM
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Summary: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.
ISBN:9783642284922
3642284922
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-28493-9_34