通信学报 ›› 2013, Vol. 34 ›› Issue (7): 159-166.doi: 10.3969/j.issn.1000-436x.2013.07.018

• 学术通信 • 上一篇    下一篇

基于成对约束Info-Kmeans聚类的图像索引方法

刘文杰1,伍之昂2,曹杰2,潘金贵2   

  1. 1 南京大学 软件新技术国家重点实验室,江苏 南京 210046;
    2 南京财经大学 江苏省电子商务重点实验室,江苏 南京 210003
  • 出版日期:2013-07-25 发布日期:2017-06-24
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;江苏省省属高校自然科学研究重大基金资助项目;国家科技支撑计划基金资助项目;江苏省自然科学基金资助项目;江苏省自然科学基金资助项目

Image indexing method based on clustering via Info-Kmeans under pair constraints

Wen-jie LIU1,Zhi-ang WU2,Jie CAO2,Jin-gui PAN2   

  1. 1 State Key Lab for Novel Software Technology,Nanjing University,Nanjing 210046,China;
    2 Jiangsu Provincial Key Laboratory of E-Business,Nanjing University of Finance and Economics,Nanjing 210003,China
  • Online:2013-07-25 Published:2017-06-24
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;Key Project of Natural Science Research of Jiangsu Provincial Colleges and Universities;National Key Technologies R&D Program of China;The Natural Science Foundation of Jiangsu Province;The Natural Science Foundation of Jiangsu Province

摘要:

针对图像数据噪声大和高维稀疏的特点,提出了一种基于噪声过滤和Info-Kmeans 聚类的图像索引构建方法。首先,利用余弦兴趣模式过滤噪声。其次,提出了一种新的Info-Kmeans聚类算法,该算法不仅避免KL-divergence计算过程中的零值困境问题,还能融合以成对约束出现的先验知识。最后,在LFW和Oxford_5K 2个图像数据集上的实验表明:噪声过滤能显著提高聚类性能;Info-Kmeans比已有聚类工具具有更优越的性能。

关键词: 图像索引, 兴趣模式, 噪声过滤, 聚类分析

Abstract:

Constructing high-quality content-based image indexing is fairly difficult due to the large amount of noise in the data set and the high-dimension and the sparseness of the image data.To meet this challenge,a novel noise-filtering and clustering was proposed using Info-Kmeans based image indexing construction method. Firstly,a noise-filtering me-thod using the cosine interesting patterns was presented. Secondly,a novel Info-Kmeans algorithm was proposed which could avoid the zero-feature dilemma caused by the use of KL-divergence and exploit the prior knowledge in the form of pair constraints. The experimental results on the two image data sets,LFW and Oxford_5K,well demonstrate that: noise filter can improve the clustering performance remarkably and the novel Info-Kmeans algorithm yields better results than the existing clustering tool.

Key words: image indexing, nteresting pattern, noise filtering, cluster analysis

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