Wang, Jun; Song, Dawei and Kaliciak, Leszek
PDF (Version of Record)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (165Kb) | Preview
|Google Scholar:||Look up in Google Scholar|
In multimedia information retrieval, where a document may contain textual and visual content features, the ranking of documents is often computed by heuristically combining the feature spaces of different media types or combining the ranking scores computed independently from different feature spaces. In this paper, we propose a principled approach inspired by Quantum Theory. Specifically, we propose a tensor product based model aiming to represent text and visual content features of an image as a non-separable composite system. The ranking scores of the images are then computed in the form of a quantum measurement. In addition, the correlations between features of different media types are incorporated in the framework. Experiments on ImageClef2007 show a promising performance of the tensor based approach.
|Item Type:||Conference Item|
|Copyright Holders:||2010 Association for the Advancement of Artificial Intelligence|
|Academic Unit/School:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Dawei Song|
|Date Deposited:||01 Nov 2012 11:02|
|Last Modified:||30 Nov 2016 14:08|
|Share this page:|
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.