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Tensor product of correlated text and visual features: a quantum theory inspired image retrieval framework

Wang, Jun; Song, Dawei and Kaliciak, Leszek (2010). Tensor product of correlated text and visual features: a quantum theory inspired image retrieval framework. In: AAAI-Fall 2010 Symposium on Quantum Informatics for Cognitive, Social, and Semantic Processes (QI2010), 11-13 November 2010, Washington DC, USA.

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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
Extra Information: pp.109-116
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
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Item ID: 35130
Depositing User: Dawei Song
Date Deposited: 01 Nov 2012 11:02
Last Modified: 24 Feb 2016 02:59
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