Fundamentals of digital image processing
Fundamentals of digital image processing
A feedback algorithm for determining search parameters for Monte Carlo optimization
Journal of Computational Physics
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Tabu Search
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An empirical approach to grouping and segmentation
An empirical approach to grouping and segmentation
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A subjective method for image segmentation evaluation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Multi-scale image segmentation algorithm based on support vector machine approximation criteria
Concurrency and Computation: Practice & Experience
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A methodology is presented for making use of ground truth, human-segmented image data sets to compare, develop and optimize image segmentation algorithms. Central to this question is the problem of quantifying the accuracy of the match between machine and reference segmentations. In this regard, the paper introduces a natural extension to the concept of precision-recall curves, which are a standard evaluation technique in pattern recognition. Computationally efficient match measures defined so as to benefit from the availability of multiple alternative human segmentations, are also proposed. The Berkeley image segmentation data set is used to select among the proposed measures, which results in a validation of the local best fit heuristic as a way to best exploit reference segmentations. I then show how the resulting match criterion can be used to improve the recent SRM segmentation algorithm by gradual modifications and additions. In particular, I demonstrate and quantify performance increases resulting from changing color coordinates, optimizing the segment merging rule, introducing texture, and forcing segments to stop at edges. As modifications to the algorithm require the optimization of parameters, a mixed deterministic and Monte-Carlo method well adapted to the problem is introduced. A demonstration of how the method can be used to compare the performance of two algorithms is made, and its broad applicability to other segmentation methods is discussed.