TY - JOUR
T1 - Biologically inspired features for scene classification in video surveillance
AU - Huang, Kaiqi
AU - Tao, Dacheng
AU - Yuan, Yuan
AU - Li, Xuelong
AU - Tan, Tieniu
PY - 2011/2
Y1 - 2011/2
N2 - 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.
AB - 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.
KW - Biologically inspired
KW - scene classification
KW - video surveillance
UR - https://www.scopus.com/pages/publications/79551681231
U2 - 10.1109/TSMCB.2009.2037923
DO - 10.1109/TSMCB.2009.2037923
M3 - 文章
C2 - 20100675
AN - SCOPUS:79551681231
SN - 1083-4419
VL - 41
SP - 307
EP - 313
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 1
M1 - 5395619
ER -