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Adaptive superpixel segmentation aggregating local contour and texture features
Xiao X.; Gong Y.-J.; Zhou Y.
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference DateMAR 05-09, 2017
Conference PlaceNew Orleans, LA

Superpixel segmentation targets at grouping pixels in an image into atomic regions that align well with the natural object boundaries. In this paper, we propose a novel superpixel segmentation method based on an iterative and adaptive clustering algorithm that embraces color, contour, texture, and spatial features together. The algorithm adjusts the weights of different features automatically in a content-aware way, so as to fit the requirements of various image instances. More specifically, in each iteration, the weights in the aggregation function are adjusted according to the discriminabilities of features in the current working scenario. This way, the algorithm not only possesses improved robustness but also relieves the burden of setting the parameters manually. Experimental verification shows that the algorithm outperforms existing peer algorithms in terms of commonly used evaluation metrics, while using a low computational cost.

KeywordAdaption Contour Superpixel Texture
URLView the original
Indexed BySCI
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000414286202016
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Xiao X.,Gong Y.-J.,Zhou Y.. Adaptive superpixel segmentation aggregating local contour and texture features[C],2017:1902-1906.
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