%0 Journal Article
%T Spatial Semantic Objects-based Hybrid Learning Method for Automatic Complicated Scene Classification<br>基于空间语义对象混合学习的复杂图像场景自动分类方法研究
%A Sun Xian
%A Fu Kun
%A Wang Hong-qi
%A <br>孙显
%A 付琨
%A 王宏琦
%J 电子与信息学报
%D 2011
%I 
%X 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.
%K Image processing
%K Scene classification
%K Semantic object
%K Hybrid learning
%K Pyramid matching<br>图像处理
%K 场景分类
%K 语义对象
%K 混合学习
%K 金字塔匹配
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=D49446C5140FA2C9422482116F69F227&yid=9377ED8094509821&vid=27746BCEEE58E9DC&iid=0B39A22176CE99FB&sid=15251AE9C02726D3&eid=0954045FA0C6885F&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=30