基于半监督距离学习和多模态信息的图像聚类

计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 41-45.

基于半监督距离学习和多模态信息的图像聚类

梁建青,胡清华

天津大学计算机科学与技术学院天津300072;天津大学计算机科学与技术学院天津300072

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家优秀青年科学基金(61222210)资助

Image Clustering Based on Semi-supervised Distance Learning and Multi-modal Information

LIANG Jian-qing and HU Qing-hua

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 通过融合图像中不同模态的信息并利用少量带标记的图像进行半监督距离学习,来对图像进行聚类。首先,提取彩色图像中RGB颜色空间的直方图信息、纹理信息,并采用SIFT算法提取Bag of Words来重新表达图像,从而基于图像的颜色特征、纹理特征以及语义特征,建立图像的多模态表达机制,将原始图像投射到表达空间；然后,利用少量标记的图像,通过半监督距离学习,获得图像在多模态信息空间的相似性度量；最后,通过半监督聚类方法,实现图像分组,在多个图像数据库中验证提出的方法的有效性。

关键词: 半监督,距离学习,多模态,图像聚类中图法分类号TP391.4文献标识码A

Abstract: The project clustered images by fusing the different model information in the images and taking advantage of a small amount of labeled images for semi-supervised distance learning．First,we extracted histogram information of the RGB color space,texture information in the color images,and Bag of Words by using the SIFT algorithm to re-express the image,thus establishing the multi-modal express mechanism of images based on the image's color,texture and semantic features to project the original image onto the space to express．Then,using a small amount of the marked ima-ge,we obtained a similarity measure in multi-modal information space of images through the semi-supervised distance learning．Finally,we realized grouping images through the semi-supervised clustering method and verified the validity of the proposed method in the plurality of images in the database as well.

Key words: Semi-supervise,Distance learning,Multi-modal,Image clustering

梁建青,胡清华. 基于半监督距离学习和多模态信息的图像聚类[J]. 计算机科学, 2014, 41(3): 41-45. https://doi.org/

LIANG Jian-qing and HU Qing-hua. Image Clustering Based on Semi-supervised Distance Learning and Multi-modal Information[J]. Computer Science, 2014, 41(3): 41-45. https://doi.org/

参考文献

[1] Hoi S C H,Liu W,Chang S F．Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval[C]∥2008IEEE Conference on Computer Vision and Pattern Recognition,2008．Anchorage,Alaska,IEEE Computer Society,2008:1-7
[2] Weinberger K Q,Blitzer J,Saul L K．Distance Metric Learning for Large Margin Nearest Neighbor Classification [J]．Journal of Machine Learning Research,2009,10:207-244
[3] Bilenko M,Basu S J,Mooney R．Integrating Constraints andMetric Learning in Semi-Supervised Clustering[C]∥ICML’04Proceedings of the 21st International Conference on Machine Learning,2004．Banff,Canada:ACM New York,2004:81-88
[4] McFee B,Lanckriet G．Learning Multi-modal Similarity [J]．Journal of Machine Learning Research,2011,12:491-523
[5] 熊建斌,李振坤,刘怡俊．半监督聚类算法研究现状[J]．现代计算机,2009,12:61-64
[6] 高惠璇．应用多元统计分析[M]．北京:北京大学出版社,2005:233-234
[7] 李有锋．基于颜色和纹理特征的图像检索相关算法研究[D]．成都:电子科技大学,2009
[8] 杨璐宇．基于图像SIFT特征的图像检索方法[J]．科技资讯,2009,34:81-82
[9] 雷兰一菲．基于局部图像特征的目标识别和分类方法研究[D]．北京:北京化工大学,2011
[10] 王耀南,李树涛,毛建旭．计算机图像处理与识别技术[M]．北京:高等教育出版社,2001:149-155
[11] 张琳波,王春恒,肖柏华,等．基于Bag-of-phrases的图像表示方法[J]．自动化学报,2012(1):46-54

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed