Name
Abstract | ||
|---|---|---|
While artists demonstrate their individual styles through paintings and drawings, how to describe such artistic styles well selected visual features towards computerized analysis of the arts remains to be a challenging research problem. In this paper, we propose an integrated feature-based artistic descriptor with Monte Carlo Convex Hull (MCCH) feature selection model and support vector machine (SVM) for characterizing the traditional Chinese paintings and validate its effectiveness via automated classification of Chinese paintings authored by well-known Chinese artists. The integrated artistic style descriptor essentially contains a number of visual features including a novel feature of painting composition and object feature, each of which describes one element of the artistic style. In order to ensure an integrated discriminating power and certain level of adaptability to the variety of artistic styles among different artists, we introduce a novel feature selection method to process the correlations and the synergy across all elements inside the integrated feature and hence complete the proposed style-based descriptor design. Experiments on classification of Chinese paintings via a parallel MCCH model illustrate that the proposed descriptor outperforms the existing representative technique in terms of precision and recall rates. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.08.013 | Neurocomputing |
Keywords | Field | DocType |
Monte Carlo,Artistic style descriptor,Feature selection,Classification of Chinese paintings | Adaptability,Monte Carlo method,Pattern recognition,Feature selection,Precision and recall,Support vector machine,Convex hull,Painting,Artificial intelligence,The arts,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
171 | 0925-2312 | 2 |
PageRank | References | Authors |
0.38 | 23 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meijun Sun | 1 | 74 | 11.77 |
| Dong Zhang | 2 | 9 | 2.02 |
| Zheng Wang | 3 | 35 | 3.40 |
| Jinchang Ren | 4 | 1144 | 88.54 |
| Jesse S. Jin | 5 | 705 | 85.36 |