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Saliency Detection and Object Classification

Cooley, Christopher, Coleman, SA, Gardiner, Bryan and Bryan, Scotney (2017) Saliency Detection and Object Classification. In: Irish Machine Vision and Image Processing, Maynooth. NUI Maynooth. 7 pp. [Conference contribution]

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Abstract

Humans have a distinct ability to process only the information that is of interest within a scene, however, this is not an easy task for computers. Trying to replicate this behaviour, many methods have been proposed to generate saliency maps that segment the object of interest within an image. In this paper, we investigate the problem of object classification, and whether saliency detection can be used. We generate saliency maps produced by two different currently published saliency detection methods, and train separate linear SVMs using the feature vectors obtained from these methods. We evaluate these methods against the traditional approach of extracting features from an image for object classification, namely HoG features. Our results show that saliency detection can be used for object classification, and improves accuracy by 5%.

Item Type:Conference contribution (Paper)
Keywords:Image Processing, Saliency Detection, Classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:38412
Deposited By: Dr Sonya Coleman
Deposited On:04 Aug 2017 08:14
Last Modified:17 Oct 2017 16:30

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