ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Model Order Selection and Cue Combination for Image Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Unsupervised performance evaluation of image segmentation
EURASIP Journal on Applied Signal Processing
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Ensemble Combination for Solving the Parameter Selection Problem in Image Segmentation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Performance evaluation of image segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic selection of parameters for vessel/neurite segmentation algorithms
IEEE Transactions on Image Processing
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This paper addresses the parameter selection problem in image segmentation. Mostly, segmentation algorithms have parameters which are usually fixed beforehand by the user. Typically, however, each image has its own optimal set of parameters and in general a fixed parameter setting may result in unsatisfactory segmentations. In this paper we present a novel unsupervised framework for automatically choosing parameters based on a comparison with the results from some reference segmentation algorithm(s). The experimental results show that our framework is even superior to supervised selection method based on ground truth. The proposed framework is not bounded to image segmentation and can be potentially applied to solve the adaptive parameter selection problem in other contexts.