Abstract
This paper proposes a new color-texture texton based approach for roadside vegetation classification in natural images. Two individual sets of class-semantic textons are first generated from color and filter bank texture features for each class. The color and texture features of testing pixels are then mapped into one of the generated textons using the nearest distance, resulting in two texton occurrence matrices – one for color and one for texture. The classification is achieved by aggregating color-texture texton occurrences over all pixels in each over-segmented superpixel using a majority voting strategy. Our approach outperforms previous benchmarking approaches and achieves 81% and 74.5% accuracies of classifying seven objects on a cropped region dataset and six objects on an image dataset collected by the Department of Transport and Main Roads, Queensland, Australia.
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Acknowledgments
This research was supported under Australian Research Council’s Linkage Projects funding scheme (project number LP140100939).
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Zhang, L., Verma, B., Stockwell, D. (2015). Class-Semantic Color-Texture Textons for Vegetation Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_39
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DOI: https://doi.org/10.1007/978-3-319-26532-2_39
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