Title: Using holistic features for scene classification by combining classifiers
Authors: de Souza Gazolli, Kelly Assis
Salles, Evandro Ottoni Teattini
Citation: Journal of WSCG. 2013, vol. 21, no. 1, p. 41-48.
Issue Date: 2013
Publisher: Václav Skala - Union Agency
Document type: článek
URI: http://wscg.zcu.cz/WSCG2013/!_2013_J_WSCG-1.pdf
ISSN: 1213–6972 (hardcopy)
1213-6980 (CD-ROM)
1213–6964 (on-line)
Keywords: klasifikace scény;počítačové vidění;vizuální deskriptory
Keywords in different language: scene classification;computer vision;visual descriptors
Abstract: Scene classification is a useful, yet challenging problem in computer vision. Two important tasks for scene classification are the image representation and the choice of the classifier used for decision making. This paper proposes a new technique for scene classification using combined classifiers method. We run two classifiers based on different features: GistCMCT and spatial MCT and combine the output results to obtain the final class. In this way, we improve accuracy, by taking advantage from the qualities of the two descriptors, without increasing the final size of the feature vector. Experimental results on four used datasets demonstrate that the proposed methods could achieve competitive performance against previous methods.
Rights: © Václav Skala - UNION Agency
Appears in Collections:Volume 21, Number 1 (2013)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/6868

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