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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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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
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:
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
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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