Huang Kaiqi, Tao Dacheng, Yuan Yuan, Li Xuelong, Tan Tieniu
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
IEEE Trans Syst Man Cybern B Cybern. 2011 Feb;41(1):307-13. doi: 10.1109/TSMCB.2009.2037923. Epub 2010 Jan 22.
Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective , and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.
受人类视觉认知机制的启发，本文首先提出了一种基于改进的标准模型特征的场景分类方法。与场景分类方面的现有先进成果相比，新提出的方法更稳健、更具选择性且复杂度更低。在我们自己的数据库和标准公共数据库上进行的两组实验证明了这些优势。此外，本文还首次研究了视频监控中场景分类的遮挡和无序问题。
