Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give
the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel
framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity
and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos,
multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is
computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector
for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement
boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency
and robustness of our proposed method.
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