Integrating semantic templates with decision tree for image semantic learning

Authors:
Ying Liu;Dengsheng Zhang;Guojun Lu;Ah-Hwee Tan
Affiliations:
Emerging Research Lab, School of Computer Engineering, Nanyang Technological University, Singapore;Gippsland School of Computing and Information Technology, Monash University, Vic, Australia;Gippsland School of Computing and Information Technology, Monash University, Vic, Australia;Emerging Research Lab, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Year:
2007

Citing 7
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Induction of Decision Trees

Machine Learning
Multi-interval Discretization Methods for Decision Tree Learning

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Metric for Distributions with Applications to Image Databases

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Region-Based image retrieval with perceptual colors

PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II

Graph object oriented database for semantic image retrieval

ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems

Quantified Score

Hi-index	0.00

Visualization

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.