Magalhães, João and Rüger, Stefan
(2010).
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| DOI (Digital Object Identifier) Link: | http://doi.org/10.1145/1852102.1852105 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types.
We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models.
| Item Type: | Journal Article |
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| Copyright Holders: | 2010 ACM |
| ISSN: | 1558-2868 |
| Extra Information: | This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. |
| Keywords: | algorithms; measurement; experimentation; indexing; search, multimedia; automated keyword annotation |
| Academic Unit/Department: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Item ID: | 28193 |
| Depositing User: | Stefan Rüger |
| Date Deposited: | 16 Feb 2011 12:18 |
| Last Modified: | 06 Aug 2016 04:13 |
| URI: | http://oro.open.ac.uk/id/eprint/28193 |
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