[CVPR 2013] Lp-norm IDF for Large Scale Image Search

[CVPR 2013] Lp-norm IDF for Large Scale Image Search

ID:40702260

大小:1.41 MB

页数:8页

时间:2019-08-06

[CVPR 2013] Lp-norm IDF for Large Scale Image Search_第1页
[CVPR 2013] Lp-norm IDF for Large Scale Image Search_第2页
[CVPR 2013] Lp-norm IDF for Large Scale Image Search_第3页
[CVPR 2013] Lp-norm IDF for Large Scale Image Search_第4页
[CVPR 2013] Lp-norm IDF for Large Scale Image Search_第5页
资源描述:

《[CVPR 2013] Lp-norm IDF for Large Scale Image Search》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库

1、2013IEEEConferenceonComputerVisionandPatternRecognitionLp-normIDFforLargeScaleImageSearchLiangZheng1,ShengjinWang1,ZiqiongLiu1,andQiTian21TsinghuaUniversity,Beijing,China2UniversityofTexasatSanAntonio,TX,78249,USAzheng-l06@mails.tsinghua.edu.cnwgsgj@tsinghua.edu.cnliuziqiong@ocrserv.ee.ts

2、inghua.edu.cnqitian@cs.utsa.eduAbstractTheInverseDocumentFrequency(IDF)isprevalentlyu-tilizedintheBag-of-Wordsbasedimagesearch.Thebasicideaistoassignlessweighttotermswithhighfrequency,andviceversa.However,theestimationofvisualwordfre-quencyiscoarseandheuristic.Therefore,theeffectiv

3、enessoftheconventionalIDFroutineismarginal,andfarfromoptimal.Totacklethisproblem,thispaperintroducesanov-elIDFexpressionbytheuseofLp-normpoolingtechnique.Carefullydesigned,theproposedIDFtakesintoaccounttheFigure1.Antoyexampleofanimagecollection.Visualwordstermfrequency,documentfrequency,t

4、hecomplexityofim-zxandzybothoccursinallthesiximages,butwithvaryingT-ages,aswellasthecodebookinformation.OptimizingtheFdistributionsovertheentireimagecollection.InconventionalIDFfunctiontowardsoptimalbalancingbetweenTFandIDF,theIDFweightsareequaltozeroforbothwords.ButwhenpIDFweightsyieldst

5、heso-calledLp-normIDF(pIDF).resortingtoTF,zxandzybothhavesomediscriminativepower,theproblemofwhichwillbetackledinthispaper.WeshowthattheconventionalIDFisaspecialcaseofourgeneralizedversion,andtwonovelIDFs,i.e.theaverageIDFandthemaxIDF,canalsobederivedfromourfor-mula.Further,bycountingfort

6、heterm-frequencyineachisthemostpopularandperhapsthemostsuccessfulone.image,theproposedLp-normIDFhelpstoalleviatethevi-Thismodelstartsfromtheextractionofsalientlocalre-sualwordburstinessphenomenon.gionsfromanimageandrepresentingeachlocalpatchasahigh-dimensionalfeaturevector(e.g.SIFT[7]orit

7、svariantsOurmethodisevaluatedthroughextensiveexperiments[13]).Thenthecontinuoushighdimensionalfeaturespaceonthreebenchmarkdatasets(Oxford5K,Paris6Kandisdividedintoadiscretespaceofvisualwords.ThisstepisFlickr1M).Wereportaperformanceimprovementofasachievedbyconstructi

当前文档最多预览五页,下载文档查看全文

此文档下载收益归作者所有

当前文档最多预览五页,下载文档查看全文
温馨提示:
1. 部分包含数学公式或PPT动画的文件,查看预览时可能会显示错乱或异常,文件下载后无此问题,请放心下载。
2. 本文档由用户上传,版权归属用户,天天文库负责整理代发布。如果您对本文档版权有争议请及时联系客服。
3. 下载前请仔细阅读文档内容,确认文档内容符合您的需求后进行下载,若出现内容与标题不符可向本站投诉处理。
4. 下载文档时可能由于网络波动等原因无法下载或下载错误,付费完成后未能成功下载的用户请联系客服处理。