Scene Classification Based on Global Optimized Framework
JIN Tai-Song1,LI Ling-Ling2,LI Cui-Hua1
1.School of Information Science and Technology,Xiamen University,Xiamen 361005 2.Department of Computer Science and Application,Zhengzhou Institute of Aeronautical Industry Management,Zhengzhou 450015
Abstract：A scene classification algorithm based on global optimized framework is proposed. Firstly,the global scene feature named spatial envelop is obtained from the whole image,the visual word of each image block is extracted,and latent variable is defined to represent the semantic feature of the extracted visual word. Secondly,the structure graph of latent state is introduced to represent the context of visual words. In respect to scene classification strategy,objective function consisting of different potential functions is constructed in which potential functions are defined to measure the relevance of the variables including global scene feature,latent variables and scene category. Finally,the scene category of the image is determined when the global optimized solution of objective function is obtained. The experiments on the standard dataset demonstrate that the proposed algorithm achieves better results than the state-of-the-art algorithms.
金泰松，李玲玲，李翠华. 基于全局优化策略的场景分类算法[J]. 模式识别与人工智能, 2013, 26(5): 440-446.
JIN Tai-Song,LI Ling-Ling,LI Cui-Hua. Scene Classification Based on Global Optimized Framework. , 2013, 26(5): 440-446.
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2013, Vol. 26 
