Abstract

Following the fact that a large collection of ink and wash paintings (IWP) is being digitized and made available on the Internet, their automated content description, analysis, and management are attracting attention across research communities. While existing research in relevant areas is primarily focused on image processing approaches, a style-based algorithm is proposed to classify IWPs automatically by their authors. As IWPs do not have colors or even tones, the proposed algorithm applies edge detection to locate the local region and detect painting strokes to enable histogram-based feature extraction and capture of important cues to reflect the styles of different artists. Such features are then applied to drive a number of neural networks in parallel to complete the classification, and an information entropy balanced fusion is proposed to make an integrated decision for the multiple neural network classification results in which the entropy is used as a pointer to combine the global and local features. Evaluations via experiments support that the proposed algorithm achieves good performances, providing excellent potential for computerized analysis and management of IWPs.

Highlights

  • Ink and wash paintings (IWP) are distinguished from Western art in that it is executed with the Chinese brush, Chinese ink, mineral, and vegetable pigments

  • Based on the above analysis of our empirical studies, we propose a style-based arts classification algorithm as described in Fig. 3, where both global features and local features are extracted from IWPs to drive an entropy balanced neural network classifier to complete the classification of IWPs

  • We described an algorithm for automatic recognition of traditional Chinese artists via classification of their IWPs

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Summary

Introduction

Ink and wash paintings (IWP) are distinguished from Western art in that it is executed with the Chinese brush, Chinese ink, mineral, and vegetable pigments.

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