Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Image thresholding: some new techniques
Signal Processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Meta-Evaluation of Image Segmentation Using Machine Learning
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
Distance measures for image segmentation evaluation
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
Short communication: An evaluation metric for image segmentation of multiple objects
Image and Vision Computing
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
Image Segmentation - A Survey of Soft Computing Approaches
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Objective evaluation of video segmentation quality
IEEE Transactions on Image Processing
Performance measures for video object segmentation and tracking
IEEE Transactions on Image Processing
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One of the greatest challenges while working on image segmentation algorithms is a comprehensive measure to evaluate their accuracy. Although there are some measures for doing this task, but they can consider only one aspect of segmentation in evaluation process. The performance of evaluation measures can be improved using a combination of single measures. However, combination of single measures does not always lead to an appropriate criterion. Besides its effectiveness, the efficiency of the new measure should be considered. In this paper, a new and combined evaluation measure based on genetic programming (GP) has been sought. Because of the nature of evolutionary approaches, the proposed approach allows nonlinear and linear combinations of other single evaluation measures and can search within many and different combinations of basic operators to find a good enough one. We have also proposed a new fitness function to make GP enable to search within search space effectively and efficiently. To test the method, Berkeley and Weizmann datasets besides several different experiments have been used. Experimental results demonstrate that the GP based approach is suitable for effective combination of single evaluation measures.