ACM Computing Surveys (CSUR)
Clustering validity checking methods: part II
ACM SIGMOD Record
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
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
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Approximation Degrees in Decision Reduct-Based MRI Segmentation
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Handbook of Granular Computing
Handbook of Granular Computing
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
A rough set-based magnetic resonance imaging partial volume detection system
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Matching 2d image segments with genetic algorithms and approximation spaces
Transactions on Rough Sets V
Probabilistic rough entropy measures in image segmentation
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Rough entropy hierarchical agglomerative clustering in image segmentation
Transactions on rough sets XIII
Subspace entropy maps for rough extended framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
RECA components in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Uniform RECA transformations in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
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High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough - fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12,13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.