Adaptively Combining Local with Global Information for Natural Scenes Categorization

Shuoyan LIU  De XU  Xu YANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.7   pp.2087-2090
Publication Date: 2008/07/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.7.2087
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
scene classification,  pLSA,  bag-of-visterms,  

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This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.