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Inexpensive fusion methods for enhancing feature detection

Wilkins, Peter and Adamek, Tomasz and O'Connor, Noel E. and Smeaton, Alan F. (2007) Inexpensive fusion methods for enhancing feature detection. Signal Processing: Image Communication, 22 (7-8). pp. 635-650. ISSN 0923-5965

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Abstract

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Feature detection; Data fusion; TRECVID;
Subjects:Engineering > Signal processing
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > Adaptive Information Cluster (AIC)
Publisher:Elsevier
Official URL:http://dx.doi.org/10.1016/j.image.2007.05.012
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Commission FP6-027026, Science Foundation Ireland, SFI 03/IN.3/I361
ID Code:209
Deposited On:04 Mar 2008 by DORAS Administrator. Last Modified 05 May 2010 11:19

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