Spatial Semantic Objects-based Hybrid Learning Method for Automatic Complicated Scene Classification
Sun Xian Fu Kun Wang Hong-qi
(Institute of Electronic, Chinese Academy of Sciences, Beijing 100190, China) (Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China)
Abstract：Scene image classification refers to the task of grouping different images into semantic categories. A new spatial semantic objects-based hybrid learning method is proposed to overcome the disadvantages existing in most of the relative methods. This method uses generative model to deal with the objects obtained by multi-scale segmentation instead of whole image, and calculates kinds of visual features to mine the category information of every objects. Then, an intermediate vector is generated using spatial-pyramid matching algorithm, to describe both the layer data and semantic information and narrow down the “semantic gap”. The method also combines a discriminative learning procedure to train a more confident classifier. Experimental results demonstrate that the proposed method can achieve high training efficiency and classification accuracy in interpreting manifold and complicated images.
2011, Vol. 33 
