Algorithms for clustering data
Algorithms for clustering data
Introduction to algorithms
A deterministic annealing approach to clustering
Pattern Recognition Letters
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Data clustering using a model granular magnet
Neural Computation
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Multiscale annealing for grouping and unsupervised texture segmentation
Computer Vision and Image Understanding
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Data Resampling for Path Based Clustering
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Clustering Using Normalized Path-Based Metric
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
A Novel Path-Based Clustering Algorithm Using Multi-dimensional Scaling
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Intrinsic dimension induced similarity measure for clustering
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
International Journal of Intelligent Systems
Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
Advances in Data Analysis and Classification
How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Most cost function based clustering or partitioning methods measure the compactness of groups of data. In contrast to this picture of a point source in feature space, some data sources are spread out on a low-dimensional manifold which is embedded in a high dimensional data space. This property is adequately captured by the criterion of connectedness which is approximated by graph theoretic partitioning methods. We propose in this paper a pairwise clustering cost function with a novel dissimilarity measure emphasizing connectedness in feature space rather than compactness. The connectedness criterion considers two objects as similar if there exists a mediating intra cluster path without an edge with large cost. The cost function is optimized in a multi-scale fashion. This new path based clustering concept is applied to segment textured images with strong texture gradients based on dissimilarities between image patches.