计算机科学 ›› 2015, Vol. 42 ›› Issue (9): 13-17.doi: 10.11896/j.issn.1002-137X.2015.09.003

• 第十届和谐人机环境联合学术会议 • 上一篇    下一篇

图像检索系统中的缩放功能

章进洲   

  1. 南京理工大学计算机科学与工程学院 南京210000
  • 出版日期:2018-11-14 发布日期:2018-11-14

Zoom Feature in Image Retrieval System

ZHANG Jin-zhou   

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

摘要: 图像检索系统是用户导向的。根据用户意图的不同,检索结果的离散度对用户的体验有着不同的影响。一些情况下,用户希望得到的是“类而不同”的结果。当前以关键字为基础的检索系统并不能很好地捕捉到用户的意图。因此,新的交互内容——缩放比例被引入检索系统,以消除用户的意图与检索结果离散度之间的隔阂,使用户根据自己的意图直接调整检索的结果。首先得到检索系统返回的图像,之后计算图像间的视觉与语义的相似度,再利用层次聚类得到聚类树,最后通过得到用户直接调节的缩放比例,来控制聚类树展开与否。对于每棵展开的子树,选择在原检索结果中拥有最小索引值的节点作为代表。

关键词: 图像检索,相关反馈,离散度,层次聚类

Abstract: Image retrieval systems are user-oriented.Diversity of retrieval results has different effects on users’ experie-nces depending on their intents.Some users may need those different but similar results,which means higher diversity.Nevertheless current retrieval system which is majorly based on query keywords can hardly capture users’ intents directly from their query.Thus,a new interactive element,zoom factor,was introduced into retrieval system to bridge the gap between users’ intents and the diversity of retrieval results.This enables users to directly control the diversity of results.We first obtained images returned by retrieval system.And then the visual and semantic distances of each other were computed.Hierarchical clustering was then used to form a clustering tree.And finally we controlled the expansion of a sub-tree with users’ directly tune of zoom factor.For each expanded sub-tree,the node with the lowest index in the original results was selected as the representative.

Key words: Image retrieval,Relevance Feedback,Diversity,Hierarchical clustering

[1] Chen H,Karger D R.Less is more:probabilistic models for retrieving fewer relevant documents[C]∥Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2006.Seattle,WA,USA,2006:429-436
[2] Ritendra D,Dhiraj J,Li J,et al.Image retrieval[J].ACM Computing Surveys,2008,40(2):1-60
[3] Rui Y,Huang T S,Ortega M,et al.Relevance feedback:a power tool for interactive content-based image retrieval[J].IEEE Transactions on Circuits and Systems for Video Technology,1998,8(5):644-655
[4] Tang X,Liu K,Cui J,et al.IntentSearch:Capturing user intention for one-click internet image search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7),1342-1353
[5] Hoi C H,Lyu M R.A novel log-based relevance feedback technique in content-based image retrieval[C]∥Proceedings of the 12th Annual ACM International Conference on Multimedia,2004.New York,NY,USA,2004:24-31
[6] Zhou X S, Huang T S.Relevance feedback in image retrieval:A comprehensive review[J].Multimedia systems,2003,8(6):536-544
[7] Carbonell J,Goldstein J.The use of MMR,diversity-basedre-ranking for reordering documents and producing summaries[C]∥Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1998.Melbourne,Australia,1998:335-336
[8] Song K,Tian Y,Gao W,et al.Diversifying the image retrieval results[C]∥Proceedings of the 14th annual ACM international conference on Multimedia,2006.Santa Barbara,CA,USA,2006:707-710
[9] Wang M,Yang K,Hua X S,et al.Towards a relevant and diverse search of social images[J].IEEE Transactions on Multimedia,2010,12(8):829-842
[10] van Leuken R H,Garcia,et al.Visual diversification of imagesearch results[C]∥Proceedings of the 18th international conference on world wide web,2009.Madrid,Spain,2009:341-350
[11] Deselaers T,Ferrari V.Visual and semantic similarity in imagenet[C]∥2011 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Colorado Springs,USA,2011:1777-1784
[12] Cai D,He X,Li Z,et al.Hierarchical clustering of WWW image search results using visual,textual and link information[C]∥Proceedings of the 12th Annual ACM International Conference on Multimedia,2004.New York,NY,USA,2004:952-959
[13] Jaimes A,Chang S F,Loui,et al.Detection of non-identical duplicate consumer photographs[C]∥Proceedings of the 2003 Joint Conference of the Fourth International Conference on Multimedia, 2003.Singapore,2003,1:16-20
[14] Fisichella M,Deng F,Nejdl W.Efficient incremental near duplicate detection based on locality sensitive hashing[C]∥Database and Expert Systems Applications,2010.Bilbao,Spain,2010:152-166
[15] Oliva A,Torralba A.Modeling the shape of the scene:A holistic representation of the spatial envelope[J].International Journal of Computer Vision,2001,42(3),145-175
[16] Cilibrasi R L,Vitanyi P M.The google similarity distance[J].IEEE Transactions on Knowledge and Data Engineering,2007,19(3),370-383
[17] 孙吉贵,刘杰,赵连宇,等.聚类算法研究[J].软件学报,2008,9(1):48-61 Sun Ji-gui,Liu Jie,Zhao Lian-yu,et al.Clustering Algorithms Research[J].J ournal of Software,2008,9(1):48-61
[18] 唐朝霞,章慧,徐冬梅.一种改进的粒子群算法和相关反馈的图像检索[J].计算机科学,2011,38(10):278-280 Tang Zhao-xia,Zhang Hui,Xu Dong-mei.Image Retrieval Based on Improved PSO Algorithm and Relevance Feedback[J].Computer Science,2011,8(10):278-280

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