New edge charasteristics for scene and object classification

In this paper, we show that simple edge characteristics in images, when judiciously combined, can result in improved scene and object classification. Unlike existing methods that require a large number of training samples and complex learning schemes, our method discovers simple edge properties. We...

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Bibliographic Details
Main Authors: Shivakumara, Palaiahnakote, Rajan, Deepu, Sadananthan, Suresh Anand
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/101881
http://hdl.handle.net/10220/16904
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Institution: Nanyang Technological University
Language: English
Description
Summary:In this paper, we show that simple edge characteristics in images, when judiciously combined, can result in improved scene and object classification. Unlike existing methods that require a large number of training samples and complex learning schemes, our method discovers simple edge properties. We introduce three sets of edge properties, namely, centroid, compactness and aspect ratio of edges in the image. The combinations of these edge properties are used to discriminate among images in each class. A class representative is calculated for each class according to the average percentage of edges that satisfy the property of a particular class. This percentage for an unknown image is compared to the class representative to assign a label to it. It is shown that this simple edge properties-based method outperforms some of the state-of-the-art results on scene and object classification on standard databases.