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Position gradient and plane consistency based feature extraction
conference contribution
posted on 2018-03-07, 00:00 authored by Sujan ChowdhurySujan Chowdhury, Brijesh Verma, Ligang ZhangLigang Zhang© Springer International Publishing AG 2016.Labeling scene objects is an essential task for many computer vision applications. However, differentiating scene objects with visual similarity is a very challenging task. To overcome this challenge, this paper proposes a position gradient and plane consistency based feature which is designed to distinguish visually similar objects and improve the overall labeling accuracy. Using the proposed feature we can differentiate objects with the same histogram of the gradient as well as we can differentiate horizontal and vertical objects. Integrating the proposed feature with low-level texture features and a neural network classifier, we achieve a superior performance (82%) compared to state-of-the-art scene labeling methods on the Stanford background dataset.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Editor
Akira H; Seiichi O; Doya K; Kazushi I; Minho L; Derong LVolume
9948 LNCSStart Page
673End Page
681Number of Pages
9Start Date
2016-10-16Finish Date
2016-10-21eISSN
1611-3349ISSN
0302-9743ISBN-13
9783319466712Location
Kyoto, JapanPublisher
SpringerPlace of Publication
Cham, SwitzerlandPublisher DOI
Peer Reviewed
- Yes
Open Access
- No
Author Research Institute
- Centre for Intelligent Systems
Era Eligible
- Yes
Name of Conference
International Conference on Neural Information Processing, 23rd, (ICONIP 2016) 2016Usage metrics
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