Spatial contextual superpixel model for natural roadside vegetation classification. (December 2016)

• Record Type: Journal Article
• Authors: Zhang, Ligang; Verma, Brijesh; Stockwell, David
• Is Part Of: Pattern recognition. Volume 60(2016:Dec.)
• Page Start: 444
• Page End: 457
• Publication Date: 2016-12
• Subjects: Feature extraction -- Image segmentation -- Classification algorithms -- Object recognition -- Artificial neural networks; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.05.013
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Highlights: A novel Spatial Contextual Superpixel Model (SCSM) for natural vegetation classification. A new reverse superpixel merging strategy to progressively merge superpixels. High performance on challenging natural datasets and Stanford background data.