A clustering method based on the estimation of the probability density function and on the skeleton by influence zones. Application to image processing.
Michel HerbinNoël BonnetPhilippe VautrotPublished in: Pattern Recognit. Lett. (1996)
Keyphrases
- clustering method
- probability density function
- image processing
- density estimation
- probability density
- hierarchical clustering
- clustering algorithm
- cluster analysis
- parzen window
- kernel density estimation
- k means
- document clustering
- bhattacharyya distance
- spectral clustering
- density function
- probability distribution
- training data
- fuzzy c means
- affinity propagation
- distance function
- data sets
- expectation maximization
- high resolution
- pattern recognition
- similarity measure
- feature extraction
- computer vision