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Adaptively Combining Local with Global Information for Natural Scenes Categorization

Shuoyan LIU, De XU, Xu YANG

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.2087-2090
Publication Date
2008/07/01
Publicized
Online ISSN
1745-1361
DOI
10.1093/ietisy/e91-d.7.2087
Type of Manuscript
LETTER
Category
Image Recognition, Computer Vision

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