全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

?利用图像类标信息的自调式字典学习方法

DOI: 10.13232/j.cnki.jnju.2015.02.016, PP. 320-327

Keywords: 类标签,自调学习,字典学习

Full-Text   Cite this paper   Add to My Lib

Abstract:

?字典学习是图像分类的关键研究问题之一.现有的字典学习方法大都假设所有训练样本同等重要.实际上,训练样本由于样本之间关联性作为一种“隐藏属性”是未知的,因此,训练样本的学习顺序也与学习效果密切相关.提出一种将自调学习机制融合于字典更新过程的新型字典学习方法,在字典学习中,学习的过程并不是一次处理所有训练样例,而是从简单的训练样例学起,通过迭代逐步扩展至整个训练数据集.针对自调式过程是一种无监督式的学习这一特点,融合类标机制,利用图像类标信息进行监督,得到一种更加高效的简单样本判别方法,从而提高学习过程中反复迭代的效率.在caltech-101数据集上进行图像分类实验,并和其他几种字典学习算法进行了分析和比较,结果表明本文算法在字典表示以及分类效果上都取得了更好的效果.

References

[1]  sivicj,zissermana.videogoogle:atextretrievalapproachtoobjectmatchinginvideos.
[2]  in:ieeeinternationalconferenceoncomputervision.nice,france,2003,1470~1477.
[3]  lazebniks,schmidc,poncej.beyondbagsoffeatures:spatialpyramidmatchingfor
[4]  recognizingnaturalscenecategories.in:ieeeconferenceoncomputervisionandpatternrec-
[5]  ognition.newyork,ny,2006,2169~2178.
[6]  wangjj,yangjc,yuk,etal.locality-constrainedlinearcodingforimageclassification.
[7]  in:ieeeconferenceoncomputervisionandpatternrecognition.sanfrancisco,california,
[8]  usa,2010,3360~3367.
[9]  jiayq,huangc,darrellt.beyondspatialpyramids:receptivefieldlearningforpooled
[10]  imagefeatures.in:ieeeconferenceoncomputervisionandpatternrecognition.providence,
[11]  incrementalbayesianapproachtestedon101objectcategories.computervisionandimage
[12]  understanding,2007,106(1):59~70.
[13]  yangjc,yuk,gongyh,etal.linearspatialpyramidmatchingusingsparsecodingforimage
[14]  classification.in:ieeeconferenceoncomputervisionandpatternrecognition.miami,florida,
[15]  usa,2009,1794~1801.
[16]  mairalj,bachf,poncej,etal.onlinedictionarylearningforsparsecoding.in:international
[17]  tangy,yangyb,gaoy.self-paceddictionarylearningforimageclassification.in:proceedings
[18]  informationprocessingsystems.vancouver,bc,canada,2008,1033~1040.
[19]  jiangzl,linz,larrysd.learningadiscriminativedictionaryforsparsecodingvia
[20]  labelconsistentk-svd.in:ieeeconferenceoncomputervisionandpatternrecognition.
[21]  coloradosprings,co,usa,2011,1697~1704.
[22]  kumarmp,packerb,kollerd.self-pacedlearningforlatentvariablemodels.advances
[23]  onmachinelearning.montreal,qc,canada,2009,41~48.
[24]  leeyj,graumank.learningtheeasythingsfirst:self-pacedvisualcategorydiscovery.in:
[25]  rhodeisland,usa,2012,3370~3377.
[26]  liff,robf,pietrop.learninggenerativevisu-almodelsfromfewtrainingexamples:an
[27]  csurkag,dancecr,fanlx,etal.visualcate-gorizationwithbagsofkeypoints.in:european
[28]  conferenceoncomputervisionworkshoponstatisticallearningincomputervision.slovan-
[29]  skyostrov,prague,czechrepublic,2004,1~22.
[30]  olshausenba,fielddj.sparsecodingwithanovercompletebasisset:astrategyemployedby
[31]  v1.visionresearch,1997,37(23):3311~3325.
[32]  conferenceonmachinelearning.montreal,canada,2009:689~696.
[33]  ofthe20thacminternationalconferenceonmul-timedia,nara,japan,2012,833~836.
[34]  俞亚军,霍静,史颖欢等.ssxcs:半监督学习分类系统.南京大学学报(自然科学),2013,49(5):611~618.
[35]  mairalj,bachf,poncej,etal.superviseddictionarylearning.advancesinneural
[36]  yangjc,yuk,huangt.supervisedtranslation-invariantsparsecoding.in:ieee
[37]  conferenceoncomputervisionandpatternrec-ognition.sanfrancisco,ca,2010,3517~3524.
[38]  inneuralinformationprocessingsystems.vancouver,bc,canada,2010,1189~1197
[39]  bengioy,louradourj,collobertr,etal.curriculumlearning.in:internationalconference
[40]  ieeeconferenceoncomputervisionandpatternrecognition.coloradosprings,co,
[41]  usa,2011,1721~1728.
[42]  leeh,battlea,rainar,etal.efficientsparsecodingalgorithms.advancesinneuralin-
[43]  formationprocessingsystems.vancouver,bc,canada

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133