Category Constrained Learning Model for Scene Classification

Yingjun TANG  De XU  Guanghua GU  Shuoyan LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.2   pp.357-360
Publication Date: 2009/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.357
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
scene classification,  pLSA,  LDA,  CC-LDA,  

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We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.