Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31617–31632 | Cite as

A New Automatic Visual Scene Segmentation Algorithm for Flash Movie

  • Lin ShiEmail author
  • Zengxiao Chi
  • Xiangzeng Meng


Flash movie retrieval system can improve the utilization of Flash movie on the Internet. The key step of a content-based Flash movie retrieval system is visual scene segmentation that directly affects the retrieval effect. In this paper, an adaptive threshold method for visual scene segmentation based on frame difference of color histogram is proposed. Firstly, all key frame sequences of a Flash movie are obtained; then the region-weighted color histogram difference of adjacent key frames is calculated; lastly, the visual scene is classified by comparing the result with the average difference. In the process of visual scene segmentation, the spatial characteristics of color are considered and the regional weighting coefficient of key frames is determined by comparing the experiments. The proposed algorithm replaces the traditional fixed global threshold with a variable adaptive threshold. The experiments show that the proposed algorithm has a better detection effect than the fixed threshold algorithm. This algorithm can easily be implemented with moderate computational complexity. The proposed algorithm can be used to extract visual features of the visual scene, generate dynamic summary, and finally can be applied to content-based Flash animation retrieval system. Moreover, the proposed algorithm can also be used in non-flash applications.


Flash retrieval Visual scene Flash movie Adaptive threshold 



The work is supported by National Natural Science Foundation of China (61502259), and cooperative project “Tomato Department Store--Implementation Design of Campus New Retail E-commerce Mode in College”.

Compliance with ethical standards

Conflict of interest

The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Business SchoolShandong Jianzhu UniversityJinanChina
  2. 2.Faculty of EducationShandong Normal UniversityJinanChina
  3. 3.School of Information Science and Electrical EngineeringShandong Jiaotong UniversityJinanChina
  4. 4.School of Journalism and CommunicationShandong Normal UniversityJinanChina

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