Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (3): 564-575.doi: 10.1007/s11390-020-0246-3

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2020 • Previous Articles     Next Articles

Automatic Video Segmentation Based on Information Centroid and Optimized SaliencyCut

Hui-Si Wu1,*, Meng-Shu Liu1, Lu-Lu Yin1, Ping Li2, Zhen-Kun Wen1,*, Hon-Cheng Wong3        

  1. 1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
    2 Department of Computing, The Hong Kong Polytechnic University, Hong Kong 999077, China;
    3 Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
  • Received:2020-01-03 Revised:2020-03-22 Online:2020-05-28 Published:2020-05-28
  • Contact: Hui-Si Wu, Zhen-Kun Wen E-mail:hswu@szu.edu.cn;wenzk@szu.edu.cn
  • About author:Hui-Si Wu received his B.E. and M.E. degrees both in computer science from the Xi'an Jiaotong University (XJTU), Xi'an, in 2004 and 2007, respectively. He obtained his Ph.D. degree in computer science from The Chinese University of Hong Kong (CUHK), Hong Kong, in 2011. He is currently an associate professor in the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen. His research interests include computer graphics, image processing, and medical imaging.
  • Supported by:
    This work was supported in part by the Major Project of the New Generation of Artificial Intelligence of National Key Research and Development Project, Ministry of Science and Technology of China under Grant No. 2018AAA0102900, the National Natural Science Foundation of China under Grant Nos. 61572328 and 61973221, the Natural Science Foundation of Guangdong Province of China under Grant Nos. 2018A030313381 and 2019A1515011165, and The Hong Kong Polytechnic University under Grant Nos. P0030419 and P0030929.

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid (IC) detection according to level balance principle in physical theory. Unlike the existing methods, the image information of another dimension is provided by the IC to enhance the video segmentation accuracy. Specifically, our IC is implemented based on the information-level balance principle in the image, and denoted as the information pivot by aggregating all the image information to a point. To effectively enhance the saliency value of the target object and suppress the background area, we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the image. Then saliency maps for all frames in the video are calculated based on the detected IC. By applying IC smoothing to enhance the optimized saliency detection, we can further correct the unsatisfied saliency maps, where sharp variations of colors or motions may exist in complex videos. Finally, we obtain the segmentation results based on IC-based saliency maps and optimized SaliencyCut. Our method is evaluated on the DAVIS dataset, consisting of different kinds of challenging videos. Comparisons with the state-of-the-art methods are also conducted to evaluate our method. Convincing visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.

Key words: automatic video segmentation; information centroid; saliency detection; optimized SaliencyCut;

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