[1]李志欣,李灵芝,张灿龙.基于模糊关联规则和决策树的图像自动标注[J].智能系统学报编辑部,2015,10(04):636-643.[doi:10.3969/j.issn.1673-4785.201505009]
 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(04):636-643.[doi:10.3969/j.issn.1673-4785.201505009]
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基于模糊关联规则和决策树的图像自动标注(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年04期
页码:
636-643
栏目:
出版日期:
2015-08-25

文章信息/Info

Title:
Automatic image annotation based on fuzzy association rules and decision trees
作者:
李志欣12 李灵芝1 张灿龙12
1. 广西师范大学 广西多源信息挖掘与安全重点实验室, 广西 桂林 541004;
2. 广西信息科学实验中心, 广西 桂林 541004
Author(s):
LI Zhixin12 LI Lingzhi1 ZHANG Canlong12
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
关键词:
锐利边界模糊分类图像自动标注模糊关联规则决策树
Keywords:
sharp boundaryfuzzy classificationautomatic image annotationfuzzy association rulesdecision tree
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201505009
文献标志码:
A
摘要:
传统的基于关联规则算法的图像自动标注存在“锐利边界”问题,使分类存在模糊性、不准确性。且随着多媒体技术的飞速发展,图像信息数据迅速增长,海量的图像数据会形成大量冗余的关联规则,这将导致分类效率大大降低。针对这2个问题,文中提出基于模糊关联规则和决策树的图像自动标注模型。该模型首先获得关联训练图像低层特征和高层语义的模糊关联规则,再利用决策树方法删减冗余的模糊关联规则,基于决策树删减后的模糊关联规则,大大减小了算法的计算复杂度。实验在Corel 5k和IAPR-TC12两个基准数据集上进行,并从精度、召回率、F-measure以及产生的规则数量几个度量措施上进行比较。与其他几种前沿的图像自动标注方法的结果对比表明,该方法在图像的标注精度和标注效率上有很大的提高。
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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备注/Memo

备注/Memo:
收稿日期:2015-05-06;改回日期:。
基金项目:国家自然科学基金资助项目(61165009,61262005,61363035,61365009);国家973计划资助项目(2012CB326403);广西自然科学基金资助项目(2012GXNSFAA053219,2013GXNSFAA019345,2014GXNSFAA118368).
作者简介:李志欣,男,1971年生,副教授,博士,主要研究方向为图像理解、机器学习、多媒体分析与检索。发表学术论文40余篇,其中SCI收录5篇,EI收录30篇;李灵芝,女,1987年生,硕士研究生,主要研究方向为图像理解、机器学习;张灿龙,男,1975年生,副教授,博士,主要研究方向为模式识别、图像目标跟踪。
通讯作者:李志欣.E-mail:lizx@gxnu.edu.cn.
更新日期/Last Update: 2015-08-28