title: Biologically inspired features for scene classification in video surveillance.
abstract: 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.
authors.affiliation: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China.
authors.forename: Kaiqi
authors.initials: K
authors.lastname: Huang
authors.forename: Dacheng
authors.initials: D
authors.lastname: Tao
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yuan
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
authors.forename: Tieniu
authors.initials: T
authors.lastname: Tan
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D014741:Video Recording / Q000379:methods*; D014796:Visual Perception