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Semantic Mining Technologies for Multimedia Databases最新文献

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Similarity Learning for Motion Estimation 运动估计的相似学习
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH005
S. Zhou, Jie Shao, B. Georgescu, D. Comaniciu
{"title":"Similarity Learning for Motion Estimation","authors":"S. Zhou, Jie Shao, B. Georgescu, D. Comaniciu","doi":"10.4018/978-1-60566-188-9.CH005","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH005","url":null,"abstract":"AbstrAct","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval 高效人工图像标注与检索的形式化模型与混合方法
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH012
Rong Yan, A. Natsev, Murray Campbell
{"title":"Formal Models and Hybrid Approaches for Efficient Manual Image Annotation and Retrieval","authors":"Rong Yan, A. Natsev, Murray Campbell","doi":"10.4018/978-1-60566-188-9.CH012","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH012","url":null,"abstract":"Although.important.in.practice,.manual.image.annotation.and.retrieval.has.rarely.been.studied.by.means. of.formal.modeling.methods..In.this.chapter,.the.authors.propose.a.set.of.formal.models.to.characterize.the.annotation.times.for.two.commonly-used.manual.annotation.approaches,.that.is,.tagging.and. browsing..Based.on.the.complementary.properties.of.these.models,.the.authors.design.new.hybrid.approaches, called frequency-based annotation and learning-based annotation, to improve the efficiency of.manual.image.annotation.as.well.as.retrieval..Both.our.simulation.and.experimental.results.show.that. the.proposed.algorithms.can.achieve.up.to.a.50%.reduction.in.annotation.time.over.baseline.methods. for manual image annotation, and produce significantly better annotation and retrieval results in the same.amount.of.time.","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126943650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Shape Matching for Foliage Database Retrieval 树叶数据库检索中的形状匹配
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH004
Haibin Ling, D. Jacobs
{"title":"Shape Matching for Foliage Database Retrieval","authors":"Haibin Ling, D. Jacobs","doi":"10.4018/978-1-60566-188-9.CH004","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH004","url":null,"abstract":"Short overview of the Chapter—Computer-aided foliage image retrieval systems have the potential to dramatically speed up the process of plant species identification. Despite previous research, this problem remains challenging due to the large intra-class variability and inter-class similarity of leaves. This is particularly true when a large number of species are involved. In this chapter, we present a shape-based approach, the inner-distance shape context, as a robust and reliable solution. We show that this approach naturally captures part structures and is appropriate to the shape of leaves. Furthermore, we show that this approach can be easily extended to include texture information arising from the veins of leaves. We also describe a real electronic field guide system that uses our approach. The effectiveness of the proposed method is demonstrated in experiments on two leaf databases involving more than 100 species and 1000 leaves.","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121838595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Active Video Annotation 活动视频注释
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH013
Meng Wang, Xiansheng Hua, Jinhui Tang, Guo-Jun Qi
{"title":"Active Video Annotation","authors":"Meng Wang, Xiansheng Hua, Jinhui Tang, Guo-Jun Qi","doi":"10.4018/978-1-60566-188-9.CH013","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH013","url":null,"abstract":"","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"83 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120874119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Image Features from Morphological Scale-Spaces 形态学尺度空间的图像特征
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH002
S. Lefèvre
{"title":"Image Features from Morphological Scale-Spaces","authors":"S. Lefèvre","doi":"10.4018/978-1-60566-188-9.CH002","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH002","url":null,"abstract":"Multimedia data mining is a critical problem due to the huge amount of data available. Efficient and reliable.data.mining.solutions.require.both.appropriate.features.to.be.extracted.from.the.data.and.relevant. techniques to cluster and index the data. In this chapter, we deal with the first problem which is feature extraction.for.image.representation..A.wide.range.of.features.have.been.introduced.in.the.literature,. and.some.attempts.have.been.made.to.build.standards.(e.g..MPEG-7)..These.features.are.extracted.using.image.processing.techniques,.and.we.focus.here.on.a.particular.image.processing.toolbox,.namely. the.mathematical.morphology,.which.stays.rather.unknown.from.the.multimedia.mining.community,. even.if.it.offers.some.very.interesting.feature.extraction.methods..We.review.here.these.morphological. features;.from.the.basic.ones.(granulometry or pattern spectrum, differential morphological profile) to more.complex.ones.which.manage.to.gather.complementary.information.","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129455855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Active Learning for Relevance Feedback in Image Retrieval 图像检索中相关反馈的主动学习
Semantic Mining Technologies for Multimedia Databases Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-188-9.CH006
Jian Cheng, Kongqiao Wang, Hanqing Lu
{"title":"Active Learning for Relevance Feedback in Image Retrieval","authors":"Jian Cheng, Kongqiao Wang, Hanqing Lu","doi":"10.4018/978-1-60566-188-9.CH006","DOIUrl":"https://doi.org/10.4018/978-1-60566-188-9.CH006","url":null,"abstract":"AbstrAct Relevance.In.Co-SVM algorithm, color and texture are naturally considered as sufficient and.uncorrelated.views.of.an.image..SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples that disagree in the two classifiers are chose to label. The extensive experiments show that the proposed algorithm is beneficial to image retrieval.","PeriodicalId":439960,"journal":{"name":"Semantic Mining Technologies for Multimedia Databases","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132897808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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