Abstract In the indoor scene classification, the classification accuracy is affected by various interference factors caused by the complexity and diversity of the scene structure itself. Aiming at these problems, an indoor scene classification algorithm based on the information enhancement of visual sensitive area is proposed in this paper. By fusing the local features and the global features based on the visual sensitive region information, the multi-scale space-frequency fusion feature is constructed to classify the indoor scenes correctly. Experimental results on 3 testing sets show that the proposed algorithm obtains good classification results on different scene classification datasets with strong applicability.
Fund:Supported by National Natural Science Foundation of China(No.61673318,61502385), Xi'an Science and Technology Planned Project(No.CXY1509(13))
About author:: (SHI Jing, born in 1983, Ph. D. candidate, lecturer. Her research interests include scene image classification, pattern recognition and digital image processing.) (ZHU Hong(Corresponding author), born in 1963, Ph. D., professor. Her research interests include digital image processing, intelligent video surveillance and pattern recognition.) (WANG Jing, born in 1985, Ph. D. candidate. Her research interests include pattern recognition and digital image processing.) (XUE Shan, born in 1988, Ph. D. candidate. His research interests include pattern recognition and digital image processing.)
SHI Jing,ZHU Hong,WANG Jing等. Indoor Scene Classification Algorithm Based on Information Enhancement of Vision Sensitive Area[J]. , 2017, 30(6): 520-529.
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2017, Vol. 30 
