Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2007
Abstract
Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic learning method, which avoids the difficult image feature discretization problem by making use of semantic template (ST) defined for each concept in our database. A ST is the representative feature of a concept, generated from the low-level features of a collection of sample regions. Experimental results on real-world images confirm the promising performance of the proposed method in image semantic learning.
Keywords
decision tree, image feature discretization, image semantic learning, semantic template
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
Proceedings, 13th International Multimedia Modeling Conference (MMM'07)
Volume
4352 LNCS
Identifier
10.1007/978-3-540-69429-8_19
City or Country
Singapore
Citation
LIU, Ying; ZHANG, Dengsheng; LU, Guojun; and TAN, Ah-hwee.
Integrating semantic templates with decision tree for image semantic learning. (2007). Proceedings, 13th International Multimedia Modeling Conference (MMM'07). 4352 LNCS,.
Available at: https://ink.library.smu.edu.sg/sis_research/6876
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