Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(375.77KB)
This is an Open Access publication published under CSC-OpenAccess Policy.

PUBLICATIONS BY COUNTRIES

Top researchers from over 74 countries worldwide have trusted us because of quality publications.

United States of America
United Kingdom
Canada
Australia
Malaysia
China
Japan
Saudi Arabia
Egypt
India
Discovering Color Styles from Fine Art Images of Impressionism
Man-Kwan Shan
Pages - 314 - 324     |    Revised - 30-09-2009     |    Published - 21-10-2009
Volume - 3   Issue - 4    |    Publication Date - October 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Image mining, Painting style, Associative classification
ABSTRACT
Content-based image retrieval (CBIR) has attracted much interest since the last decade. Finding painting styles from fine art images is useful for CBIR. However, little research has been done on the painting style mining. In this paper, we investigated the color style mining technique for fine art of Impressionism. Three design issues for the color style mining are the feature extraction, the feature representation, and the style mining algorithm. For the feature extraction and presentation, dominate colors, adjacent color combinations and some MPEG-7 color descriptors, are utilized to represent the color features. Above all, we utilize the spatial data structure, 2D string, to represent color layout descriptor. For the style mining algorithms, we proposed a two-stage color style mining scheme. The first stage discovers the common properties of paintings of the same style. The second stage discovers the discriminative properties among styles. The experiment on the art work of European Impressionist was conducted. The performance of effectiveness is measure by the classification accuracy of the proposed style mining scheme. The classification accuracy ranges from 70% to 90%.
CITED BY (4)  
1 Persson, J., & Lundbladh, M. (2015). Våga vara dig själv: Att fo¨rsta° och skapa en illusion av ljus med digitala medier.
2 Tu, M. H. (2014). A method in a meaningful candidate styles mining image library of styles frequently . Sun Yat-sen Institute of Information Engineering Thesis , 1-102.
3 V. J. Vivek , N. Chandrasekar and Y. Srinivas, “Improving Seismic Monitoring System for Small to Intermediate Earthquake Detection”, International Journal of Computer Science and Security (IJCSS), 4(3), pp. 308 – 315, 2010.
4 M. K. Shan and L. Y. Wei, “Algorithms for Discovery of Spatial Co-Orientation Patterns from Images”, Expert Systems with Applications: An International Journal, 37(8), pp. 5795–5802, 2010.
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 Socol@r  
9 ResearchGATE 
10 Libsearch 
11 Bielefeld Academic Search Engine (BASE) 
12 Scribd 
13 WorldCat 
14 SlideShare 
15 PDFCAST 
16 PdfSR 
17 Chinese Directory Of Open Access 
18 Free-Books-Online 
1 R. Agrawal and R. Srikant. “Fast Algorithms for Mining Association Rules”. In Proceedings of International Conference on Very Large Data Bases, 1994.
2 M. N. Anyanwu and S. G. Shiva. “Comparative Analysis of Serial Decision Tree Classification Algorithms”. International Journal of Computer Science and Security, 3(3) 2009.
3 S. F. Chang, T. Sikora, and A. Puri. “Overview of MPEG-7 Standard”. IEEE Transactions on Circuits Systems for Video Technology, 11(6), 2001.
4 S. K. Chang, Q. Y. Shi, and C. W. Yan. "Iconic Indexing by 2D Strings", IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(3), 1987.
5 Y. Deng, and B. S. Manjunath. “Unsupervised Segmentation of Color-Texture Regions in Images and Video”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 2001.
6 B. Gunsel, S. Sariel, and O. Icoglu. “Content Based Access to Art Paintings”. In Proceedings of IEEE International Conference on Image Processing, 2005.
7 J. Han, and Y. Fu. “Discovery of Multiple-Level Association Rules from Large Databases”. IEEE Transactions on Knowledge and Data Engineering, 11(5), 1999.
8 P. S. Hiremath and J. Pujari. “Content Based Image Retrieval based on Color, Texture and Shape features using Image and Its Complement”. International Journal of Computer Science and Security, 1(4), 2007.
9 L. Leslie, T. S. Chua and R. Jain. “Annotation of Paintings with High-level Semantic Concepts Using Transductive Inference and Ontology-based Concept Disambiguation”. In Proceedings of ACM International Conference on Multimedia, 2007.
10 C. T. Li, and M. K. Shan. “Affective Space Exploration for Impressionism Paintings”. In Proceedings of Pacific-Rim Conference on Multimedia, 2008.
11 B. Liu, W. Hsu, and Y. Ma. “Integrating Classification and Association Rule Mining”. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1998.
12 B. Liu, Y. Ma and C. K. Wong. “Classification Using Association Rules: Weaknesses and Enhancements”. In Vipin Kumar, et al, (eds), Data Mining for Scientific Applications, 2001.
13 Y. Marchenko, T. S. Chua, I. Aristarkhova. “Analysis and Retrieval of Painting Using Artistic Color Concepts”. In Proceedings of IEEE International Conference on Multimedia and Expo., 2005.
14 MPEG-7 Visual Experimentation Model (XM), Version 10.0, ISO/IEC/JTC1/SC29/WG11, Doc. N4063, 2001.
15 Overview of the MPEG-7 Standard, Version 5.0, Final Committee Draft, ISO/IEC JTC1/SC29/WG11, Doc. N4031, 2001.
16 K. Preetham and V. S. Ananthanarayana. “Discovery of Frequent Itemsets Based on Minimum Quality and Support”. International Journal of Computer science and Security, 3(3), 2009.
17 J. R. Quinlan. “C4.5: Programs for Machine Learning”, Morgan Kaufmann, San Mateo, CA (1993)
18 J. R. Quinlan. “Improved Use of Continuous Attributes in C4.5”, Journal of Artificial Intelligence Research, 4, 1996.
19 R. Sablatnig, P. Kammerer and E. Zolda. “Hierarchical Classification of Painted Portraits using Face and Brush Stroke Models”. In Proceedings of International Conference on Pattern Recognition, 1998.
20 A. Vailaya, A. T. Figueriedo, A. K. Jain and H. J. Zhang. “Image Classification for Content- Based Indexing”. IEEE Transactions on Image Processing, 10(1), 2001.
21 J. Z. Wang, J. Li, and G. Wiederhold. “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 2001.
22 L. Y. Wei, and M. K. Shan, “Efficient Mining of Spatial Co-orientation Patterns from Image Databases”. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2006.
23 O. R. Zaïane, M. Antonie and A. Coman, “Mammography Classification by an Association Rule-Based Classifier”. In Proceedings of International ACM SIGKDD Workshop on Multimedia Data Mining, 2002,
24 A. Shamir and E. Tromer, "Factoring Large Numbers with the TWIRL Device", Crypto 2003, LNCS 2729, Springer-Verlag, Aug.2003.
Mr. Man-Kwan Shan
- Taiwan
mkshan@cs.nccu.edu.tw