Incorporating feature hierarchy and boosting to achieve more effective classifier training and concept-oriented video summarization and skimming

  • Authors:

  • Hangzai Luo;Yuli Gao;Xiangyang Xue;Jinye Peng;Jianping Fan

  • Affiliations:

  • Software Engineering Institute, East China Normal University, China;University of North Carolina, Charlotte, NC;Fudan University, China;Northwestern Polytechnical University, China;University of North Carolina, Charlotte, NC

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2008

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Abstract

For online medical education purposes, we have developed a novel scheme to incorporate the results of semantic video classification to select the most representative video shots for generating concept-oriented summarization and skimming of surgery education videos. First, salient objects are used as the video patterns for feature extraction to achieve a good representation of the intermediate video semantics. The salient objects are defined as the salient video compounds that can be used to characterize the most significant perceptual properties of the corresponding real world physical objects in a video, and thus the appearances of such salient objects can be used to predict the appearances of the relevant semantic video concepts in a specific video domain. Second, a novel multi-modal boosting algorithm is developed to achieve more reliable video classifier training by incorporating feature hierarchy and boosting to dramatically reduce both the training cost and the size of training samples, thus it can significantly speed up SVM (support vector machine) classifier training. In addition, the unlabeled samples are integrated to reduce the human efforts on labeling large amount of training samples. Finally, the results of semantic video classification are incorporated to enable concept-oriented video summarization and skimming. Experimental results in a specific domain of surgery education videos are provided.