NDLI: Modelling traditional Chinese paintings for content-based image classification and retrieval

Content Provider	IEEE Xplore Digital Library
Author	Danging Zhang Pham, B. Yuefeng Li
Copyright Year	2004
Description	Author affiliation: Queensland Univ. of Technol., Brisbane, Qld., Australia (Danging Zhang; Pham, B.; Yuefeng Li)
Abstract	Content-based image retrieval (CBIR) has been investigated extensively in the past decade in order to classify and search images according to similarities derived from automatically extracted visual features, such as colours, textures and object shapes. It has now been realised that two fundamental problems in CBIR, namely, feature extraction and similarity measure, are likely to be domain specific. In this paper, we present some early results of applying CBIR to traditional Chinese paintings. Our research is motivated by three main goals: (1) to develop tools for art historians to study evolution and cross-influences of oriental paintings by automatically identifying visual artistic clues from digitised paintings; (2) to verify and further advance the existing CBIR techniques by limiting the images studied to a specific and simpler domain of traditional Chinese paintings; and (3) to verify and further advance the problem of high-dimensional data clustering (especially in relation to the "dimensional curse" problem). We present a framework for modelling traditional Chinese paintings, and examine various existing CBIR proposals and algorithms for their suitability for traditional Chinese paintings. In this paper, we also present a research agenda to study the problems of Chinese paintings classification and retrieval based on the framework.
Sponsorship	Centre for Inf. Technol. Innovation School of Inf. Technol., Deakin Univ. Apple Comput. International Federation of Automatic Control Emu Design
Starting Page	258
Ending Page	264
File Size	1220143
Page Count	7
File Format	PDF
ISBN	0769520847
DOI	10.1109/MULMM.2004.1264994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-01-05
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Painting Image classification Image retrieval Content based retrieval Art Feature extraction Raw materials Shape Proposals Clustering algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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