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Inexpensive fusion methods for enhancing feature detection
Wilkins, Peter; Adamek, Tomasz; O'Connor, Noel E.; Smeaton, Alan F.
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.
Keyword(s): Signal processing; Digital video; Feature detection; Data fusion; TRECVID
Publication Date:
2007
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Wilkins, Peter, Adamek, Tomasz, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <https://orcid.org/0000-0002-4033-9135> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <https://orcid.org/0000-0003-1028-8389> (2007) Inexpensive fusion methods for enhancing feature detection. Signal Processing: Image Communication, 22 (7-8). pp. 635-650. ISSN 0923-5965
Publisher(s): Elsevier
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/209/1/signal_process_aug_2007.pdf,
http://dx.doi.org/10.1016/j.image.2007.05.012
First Indexed: 2009-11-05 02:00:24 Last Updated: 2019-02-09 07:05:10