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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 12
3781-3787

Incremental multi-class video segmentation based on dynamic dense conditional field inference

Zhang Xiaoxiang1
Lu Xianling1
Zhou Hongjun2
1. School of IoT Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
2. Tongji University, Shanghai 200092, China
DOI: 10.19734/j.issn.1001-3695.2019.07.0312

Abstract

For the problem of the cost of calculation in multi-class segmentation based on conditional random field increases with frame numbers, this paper proposed a fast and fully dynamic algorithm for dense(fully connected) conditional random field(CRF) inference. The algorithm efficiently inferred the maximum a posteriori probability(MAP) solution for a dynamically changing dense CRF model that was applied to multi-class video segmentation. Compared with traditional dense CRF for video segmentation, this method is more suitable for incremental(in-line) robotics video segmentation. The experiment results show that the algorithm is significantly faster than the widely known standard dense CRF algorithm in the application of multi-class video segmentation and it can ensure the same accuracy with them. Several multi-class video segmentation tests confirmed the efficiency of inference of the algorithm. It should be noted that the application of this algorithm is not limited to video segmentation, it also can be used to yield similar improvements in many optimization solutions for MAP in dynamically changing models.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.07.0312
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 12
Section: Technology of Graphic & Image
Pages: 3781-3787
Serial Number: 1001-3695(2020)12-054-3781-07

Publish History

[2020-12-05] Printed Article

Cite This Article

张晓翔, 卢先领, 周洪钧. 基于动态密集条件随机场增量推理计算的多类别视频分割 [J]. 计算机应用研究, 2020, 37 (12): 3781-3787. (Zhang Xiaoxiang, Lu Xianling, Zhou Hongjun. Incremental multi-class video segmentation based on dynamic dense conditional field inference [J]. Application Research of Computers, 2020, 37 (12): 3781-3787. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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