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Aggregating pixel-level prediction and cluster-level texton occurrence within superpixel voting for roadside vegetation classification

conference contribution
posted on 07.03.2018, 00:00 authored by Ligang ZhangLigang Zhang, Brijesh Verma, David Stockwell, Sujan ChowdhurySujan Chowdhury
© 2016 IEEE.Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach that aggregates pixel-level supervised classification and cluster-level texton occurrence within a voting strategy over superpixels for vegetation classification, which takes into account both generic features in the training data and local characteristics in the testing data. Class-specific artificial neural networks are trained to predict class probabilities for all pixels, while a texton based adaptive K-means clustering process is introduced to group pixels into clusters and obtain texton occurrence. The pixel-level class probabilities and cluster-level texton occurrence are further integrated in superpixel-level voting to assign each superpixel to a class category. The proposed approach outperforms previous approaches on a roadside image dataset collected by the Department of Transport and Main Roads, Queensland, Australia, and achieves state-of-the-art performance using low-resolution images from the Croatia roadside grass dataset.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

2016-October

Start Page

3249

End Page

3255

Number of Pages

7

Start Date

24/07/2016

Finish Date

29/07/2016

ISBN-13

9781509006199

Location

Vancouver, Canada

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

International Joint Conference on Neural Networks 2016 (IJCNN 2016)