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Spatial contextual superpixel model for natural roadside vegetation classification

journal contribution
posted on 2018-07-31, 00:00 authored by Ligang ZhangLigang Zhang, Brijesh Verma, David Stockwell
In this paper, we present a novel Spatial Contextual Superpixel Model (SCSM) for vegetation classification in natural roadside images. The SCSM accomplishes the goal by transforming the classification task from a pixel into a superpixel domain for more effective adoption of both local and global spatial contextual information between superpixels in an image. First, the image is segmented into a set of superpixels with strong homogeneous texture, from which Pixel Patch Selective (PPS) features are extracted to train class-specific binary classifiers for obtaining Contextual Superpixel Probability Maps (CSPMs) for all classes, coupled with spatial constraints. A set of superpixel candidates with the highest probabilities is then determined to represent global characteristics of a testing image. A superpixel merging strategy is further proposed to progressively merge superpixels with low probabilities into the most similar neighbors by performing a double-check on whether a superpixel and its neighour accept each other, as well as enhancing a global contextual constraint. We demonstrate high performance by the proposed model on two challenging natural roadside image datasets from the Department of Transport and Main Roads and on the Stanford background benchmark dataset.

Funding

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

History

Volume

60

Start Page

444

End Page

457

Number of Pages

14

eISSN

1873-5142

ISSN

0031-3203

Publisher

Elsevier, Netherlands

Peer Reviewed

  • Yes

Open Access

  • No

Author Research Institute

  • Centre for Intelligent Systems

Era Eligible

  • Yes

Journal

Pattern Recognition