Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This paper addresses a wavelet statistical model for characterizing Chinese ink painting styles. The distinct digital profile of an artist is defined as a set of feature-tons and their distribution, which characterize the strokes and stochastic nature of the painting style. Specifically, the feature-tons is modeled by a set of high-order wavelet statistics, and the high-order correlation statistics across scales and orientations, while the feature-ton distribution is represented by a finite mixture of Gaussian models estimated by an unsupervised learning algorithm from multivariate statistical features. To measure the extent of association between an unknown painting and the captured style, the likelihood of the occurrence of the image based on the characterizing stochastic process is computed. A high likelihood indicates a strong association. The research has the potential to provide a computer-aided tool for art historians to study connections among artists or periods in the history of Chinese ink painting art.