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Automatic salient object sequence rebuilding for video segment analysis

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Abstract

Detection of salient object sequences from video data is challenging when the salient object changes between consecutive frames. In this study, we addressed the salient object sequence rebuilding problem with video segment analysis. We reformulated the problem as a binary labeling problem, analyzed the potential salient object sequences in the video using a clustering method, and separated the salient object sequence from the background by applying an energy optimization method. Our proposed approach determines whether temporal consecutive pixels belong to the same salient object sequence. The conditional random field is then learned to effectively integrate the salient features and the sequence consecutive constraints. A dynamic programming algorithm was developed to resolve the energy minimization problem efficiently. Experimental results confirmed the ability of our approach to address the salient object rebuilding problem in automatic visual attention applications and video content analysis.

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Acknowledgements

This work was supported by National Key RD Program of China (Grant No. 2016YFB100 1001), National Natural Science Foundation of China (Grant No. 61603022), and China Postdoctoral Science Foundation and Aeronautical Science Foundation of China (Grant No. 20135851042).

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Correspondence to Tie Liu.

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Liu, T., Duan, H., Shang, Y. et al. Automatic salient object sequence rebuilding for video segment analysis. Sci. China Inf. Sci. 61, 012205 (2018). https://doi.org/10.1007/s11432-016-9150-x

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Keywords

  • salient object
  • video attention
  • sequence segment analysis
  • conditional random model