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Recognition by Enhanced Bag of Words Model via Topographic ICA

Jing, Min, Wang, Hui, Clawson, Kathy, Coleman, SA, Chen, Shuwei, Liu, Jun and Scotney, Bryan (2014) Recognition by Enhanced Bag of Words Model via Topographic ICA. In: UCAmI 2014. Springer. 9 pp. [Conference contribution]

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Abstract

The Bag-of-Words (BoW) model has been increasingly applied in the field of computer vision, in which the local features are first mapped to a codebook produced by clustering method and then represented by histogram of the words. One of drawbacks in BoW model is that the orderless histogram ignores the valuable spatial relationships among the features. In this study, we propose a novel framework based on a topographic independent component analysis (TICA), which enables the geometrically nearby feature components to be grouped together thereby bridge the semantic gap in BoW model. In addition, the compact feature obtained from TICA helps to build an efficient codebook. Furthermore, we introduce a new closeness measurement based on Neighbourhood Counting Measure (NCM) to improve the k Nearest Neighbour classification. The preliminary results based on KTH and Trecvid data demonstrate the proposed TICA/NCM approach increases the recognition accuracy and improve the efficiency of BoW model.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
ID Code:30444
Deposited By: Dr Min Jing
Deposited On:21 Jan 2015 15:26
Last Modified:21 Jan 2015 15:26

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