Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
The Journal of Machine Learning Research
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Image and Vision Computing
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
Waterfall segmentation of complex scenes
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Colour information is usually not enough to segment natural complex scenes. Texture contains relevant information that segmentation approaches should consider. Martin et al. [Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5) (2004) 530-549] proposed a particularly interesting colour-texture gradient. This gradient is not suitable for Watershed-based approaches because it contains gaps. In this paper, we propose a method based on the distance function to fill these gaps. Then, two hierarchical Watershed-based approaches, the Watershed using volume extinction values and the Waterfall, are used to segment natural complex scenes. Resulting segmentations are thoroughly evaluated and compared to segmentations produced by the Normalised Cuts algorithm using the Berkeley segmentation dataset and benchmark. Evaluations based on both the area overlap and boundary agreement with manual segmentations are performed.