[1]LI Zhixin,LI Lingzhi,ZHANG Canlong.Automatic image annotation based on fuzzy association rules and decision trees[J].CAAI Transactions on Intelligent Systems,2015,10(4):636-643.[doi:10.3969/j.issn.1673-4785.201505009]
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Automatic image annotation based on fuzzy association rules and decision trees

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 4 Page number: 636-643 Column: 学术论文—机器学习 Public date: 2015-08-25
Title:
Automatic image annotation based on fuzzy association rules and decision trees
Author(s):
LI Zhixin1; 2;  LI Lingzhi1;  ZHANG Canlong1; 2
1. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China;
2. Guangxi Experiment Center of Information Science, Guilin 541004, China
Keywords:
sharp boundary; fuzzy classification; automatic image annotation; fuzzy association rules; decision tree
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201505009
Abstract:
The traditional automatic image annotation based on association rules exists the problem of sharp boundary, which makes classification more fuzzy and inaccurate. Moreover, with the rapid development of multimedia technology, the size of image data increases quickly. Massive image data will produce a lot of redundant association rules, which greatly decreases the efficiency of image classification. In order to solve these two problems, this paper proposes an automatic image annotation approach based on fuzzy association rules and decision trees. The approach firstly obtains fuzzy association rules which represent the fuzzy correlations between low-level visual features and high-level semantic concepts of training images. Then, decision tree is adopted to reduce the redundant fuzzy association rules. As a result, computational complexity of the algorithm is decreased to a large degree. Experiments were done on Corel5k and IAPR-TC12 datasets. The evaluation measures are compared from the aspects of precision, recall, F-measure and the number of rules. The experimental results show that the proposed method acquires higher accuracy and efficiency in comparison with several state-of-the-art automatic image annotation approaches.
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