Magalhães, João and Rüger, Stefan
(2007).
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| URL: | http://doi.acm.org/10.1145/1282280.1282368 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
To solve the problem of indexing collections with diverse text documents, image documents, or documents with both text and images, one needs to develop a model that supports heterogeneous types of documents. In this paper, we show how information theory supplies us with the tools necessary to develop a unique model for text, image, and text/image retrieval. In our approach, for each possible query keyword we estimate a maximum entropy model based on exclusively continuous features that were pre-processed. The unique continuous feature-space of text and visual data is constructed by using a minimum description length criterion to find the optimal feature-space representation (optimal from an information theory point of view). We evaluate our approach in three experiments: only text retrieval, only image retrieval, and text combined with image retrieval.
| Item Type: | Conference Item |
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| Copyright Holders: | 2007 ACM |
| Extra Information: | Best paper award
Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, Amsterdam, The Netherlands, July 9-11, 2007 Nicu Sebe, Marcel Worring (Eds.) ISBN 978-1-59593-733-9 |
| Keywords: | multimedia indexing; minimum description length; multi-modal categorization; information retrieval |
| Academic Unit/Department: | Knowledge Media Institute |
| Item ID: | 29588 |
| Depositing User: | Stefan Rüger |
| Date Deposited: | 28 Sep 2011 12:59 |
| Last Modified: | 24 Oct 2012 13:39 |
| URI: | http://oro.open.ac.uk/id/eprint/29588 |
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