NDLI: Incremental hybrid Bayesian network in content-based image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Baice Li Senmiao Yuan
Copyright Year	2005
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Baice Li; Senmiao Yuan)
Abstract	A learning system is one of the future directions of the evolution of content-based image retrieval (CBIR) system. Relevance feedback (RF) is a technique that enables systems to learn from users. In the past few years, this technique has been used as an effective solution for content-based image retrieval. Based on information theory, this paper proposes an incremental hybrid Bayesian network approach -IHBN, which processes the examples with decision tree. Only for those examples that cannot be processed are resorted by Bayesian network. The model constructed by IHBN can extend easily. Its size and structure change dynamically while learning. Its distinct incremental learning mechanism can not only make inductive learning possible while lacking of domain knowledge, but also depress the noise sensibility of the learning algorithm. This approach retains the interpretability of Bayesian network and decision tree, while resulting in classifiers that outperform both constituents. We propose this approach in content-based image retrieval system with relevance feedback. By comparing the performance of our approach with most conventional approaches in CBIR with relevance feedback, the experimental results demonstrate that the IHBN approach is more feasible and effective in the content-based image retrieval
Starting Page	2025
Ending Page	2028
File Size	175235
Page Count	4
File Format	PDF
ISBN	0780388852
ISSN	08407789
DOI	10.1109/CCECE.2005.1557383
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-01
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Intelligent networks Image retrieval Content based retrieval Decision trees Feedback Learning systems Information theory Probability distribution Noise reduction
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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