A critical view of pyramid segmentation algorithms
Pattern Recognition Letters
Introduction to combinatorial pyramids
Digital and image geometry
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric Approach to Vector-Valued Image Segmentation
International Journal of Computer Vision
Boundary Extraction in Natural Images Using Ultrametric Contour Maps
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Evaluating Hierarchical Graph-based Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Learning spatial relations in object recognition
Pattern Recognition Letters
Pyramid segmentation algorithms revisited
Pattern Recognition
Combining local belief from low-level primitives for perceptual grouping
Pattern Recognition
Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
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This paper presents a bottom-up approach for perceptual segmentation of natural images. The segmentation algorithm consists of two consecutive stages: firstly, the input image is partitioned into a set of blobs of uniform colour (pre-segmentation stage) and then, using a more complex distance which integrates edge and region descriptors, these blobs are hierarchically merged (perceptual grouping). Both stages are addressed using the Combinatorial Pyramid, a hierarchical structure which can correctly encode relationships among image regions at upper levels. Thus, unlike other methods, the topology of the image is preserved. The performance of the proposed approach has been initially evaluated with respect to groundtruth segmentation data using the Berkeley Segmentation Dataset and Benchmark. Although additional descriptors must be added to deal with textured surfaces, experimental results reveal that the proposed perceptual grouping provides satisfactory scores.