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Properties of optimally weighted data fusion in CBMIR

Wilkins, Peter and Smeaton, Alan F. and Ferguson, Paul (2010) Properties of optimally weighted data fusion in CBMIR. In: SIGIR 2010 - 33rd international ACM SIGIR conference on Research and development in information retrieval, 19-23 July 2010, Geneva, Switzerland. ISBN 978-1-4503-0153-4

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Abstract

Content-Based Multimedia Information Retrieval (CBMIR) systems which leverage multiple retrieval experts (En ) of- ten employ a weighting scheme when combining expert re- sults through data fusion. Typically however a query will comprise multiple query images (Im ) leading to potentially N × M weights to be assigned. Because of the large number of potential weights, existing approaches impose a hierarchy for data fusion, such as uniformly combining query image results from a single retrieval expert into a single list and then weighting the results of each expert. In this paper we will demonstrate that this approach is sub-optimal and leads to the poor state of CBMIR performance in benchmarking evaluations. We utilize an optimization method known as Coordinate Ascent to discover the optimal set of weights (|En | · |Im |) which demonstrates a dramatic difference be- tween known results and the theoretical maximum. We find that imposing common combinatorial hierarchies for data fu- sion will half the optimal performance that can be achieved. By examining the optimal weight sets at the topic level, we observe that approximately 15% of the weights (from set |En | · |Im |) for any given query, are assigned 70%-82% of the total weight mass for that topic. Furthermore we discover that the ideal distribution of weights follows a log-normal distribution. We find that we can achieve up to 88% of the performance of fully optimized query using just these 15% of the weights. Our investigation was conducted on TRECVID evaluations 2003 to 2007 inclusive and ImageCLEFPhoto 2007, totalling 181 search topics optimized over a combined collection size of 661,213 images and 1,594 topic images.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Additional Information:Nominated for best paper award at SIGIR 2010
Subjects:Computer Science > Multimedia systems
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in:Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. . Association for Computing Machinery. ISBN 978-1-4503-0153-4
Publisher:Association for Computing Machinery
Official URL:http://dx.doi.org/10.1145/1835449.1835556
Copyright Information:© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from http://dx.doi.org/10.1145/1835449.1835556
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:15370
Deposited On:28 Jul 2010 14:45 by Peter Wilkins. Last Modified 03 Aug 2010 09:42

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