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Improving content based image retrieval by identifying least and most correlated visual words

Kaliciak, Leszek; Song, Dawei; Wiratunga, Nirmalie and Pan, Jeff (2012). Improving content based image retrieval by identifying least and most correlated visual words. In: 8th Asia Information Retrieval Societies Conference (AIRS2012), 17-19 Dec 2012, Tianjin, China.

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In this paper, we propose a model for direct incorporation of im- age content into a (short-term) user profile based on correlations between visual words and adaptation of the similarity measure. The relationships between visual words at different contextual levels are explored. We introduce and compare var- ious notions of correlation, which in general we will refer to as image-level and proximity-based. The information about the most and the least correlated visual words can be exploited in order to adapt the similarity measure. The evaluation, preceding an experiment involving real users (future work), is performed within the Pseudo Relevance Feedback framework. We test our new method on three large data collections, namely MIRFlickr, ImageCLEF, and a collection from British National Geological Survey (BGS). The proposed model is computation- ally cheap and scalable to large image collections.

Item Type: Conference or Workshop Item
Copyright Holders: 2012 Springer-Verlag
Keywords: content-based image retrieval and representation; local features; correlation; pseudo relevance feedback; similarity measure
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
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Item ID: 34650
Depositing User: Dawei Song
Date Deposited: 16 Oct 2012 12:19
Last Modified: 08 Dec 2018 06:45
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