NDLI: Automatic image annotation based on decision tree machine learning

Content Provider	IEEE Xplore Digital Library
Author	Lixing Jiang Jin Hou Zeng Chen Dengsheng Zhang
Copyright Year	2009
Description	Author affiliation: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 610031, P.R. China (Lixing Jiang; Jin Hou; Zeng Chen) \|\| Gippsland School of Info Tech, Monash University, Churchill, Victoria 3842, Australia (Dengsheng Zhang)
Abstract	With the rapid development of digital imaging technology, image annotation is an important and challenging task in image retrieval. At present, many machine learning methods have been applied to solve the problem of automatic image annotation (AIA). However, there exists enormous semantic expressive gap between the low-level image features and high-level semantic concepts. Due to the problem, the annotation performance of existing methods is not satisfactory, and needs to be further improved. This paper proposes an automatic annotation framework via a novel decision tree-based Bayesian (DTB) machine learning algorithm. It is a hybrid approach that attempts to utilize the advantages of both DT and Naive-Bayesian (NB). We firstly segment an image into different regions and extract low-level features of each region. From these features, high-level semantic concepts are obtained using a DTB learning algorithm. Finally, experiments conducted on the Corel dataset demonstrate the effectiveness of DTB machine learning. The DTB can not only enhance the classification accuracy, but also associate low-level region features with high-level image concepts. This method presents the advantages of the Bayesian method and the DT. Moreover, this semantic interpretation capability is a natural simulation of human learning.
Starting Page	170
Ending Page	175
File Size	370250
Page Count	6
File Format	PDF
ISBN	9781424452187
DOI	10.1109/CYBERC.2009.5342168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Digital images Image retrieval Decision tree-based Bayesian Semantic-based image retrieval Niobium Learning systems Image segmentation Automatic image annotation Bayesian methods Machine learning Feature extraction Decision trees
Content Type	Text
Resource Type	Article

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