Image content annotation using Bayesian framework and complement components analysis
In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose t...
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| creator | Changbo Yang Ming Dong Fotouhi, F. |
| description | In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose to estimate the class conditional probabilities in the feature subspace constructed by complement components analysis (CCA). We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall. |
| doi_str_mv | 10.1109/ICIP.2005.1529970 |
| format | Conference Proceeding |
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We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall.</description><subject>Bayesian methods</subject><subject>Digital images</subject><subject>Feature extraction</subject><subject>Image analysis</subject><subject>Image classification</subject><subject>Image databases</subject><subject>Image retrieval</subject><subject>Indexing</subject><subject>Noise reduction</subject><subject>Training data</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9780780391345</isbn><isbn>0780391349</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkM1KxEAQhAd_wGXNA4iXvEBiz296jhp0DSzoIfdlkkyW0WSyZCKSt3cWt2iogv66D0XIA4WcUtBPVVl95gxA5lQyrQu4IhvGkWYohb4miS4Q4nBNuZA3ZBMplglEuCNJCF8QJaQAVWxIXY3maNN28ov1S2q8nxazuMmnP8H5Y_piVhuc8Wk_m9H-TvN3ZLrIj6fBjueTc5x8TCFuzLAGF-7JbW-GYJOLb0n99lqX79n-Y1eVz_vMaVgyahkqiQJkh6plphAIVvZSCtVioXinGQWmOt40CnreN6i4UEIorrCgneFb8vj_1llrD6fZjWZeD5dK-B_WHVLJ</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Changbo Yang</creator><creator>Ming Dong</creator><creator>Fotouhi, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Image content annotation using Bayesian framework and complement components analysis</title><author>Changbo Yang ; Ming Dong ; Fotouhi, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1e28658405d86c2a7480e5f5546c8763d921026d3bb60f3fb8634644636871da3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Bayesian methods</topic><topic>Digital images</topic><topic>Feature extraction</topic><topic>Image analysis</topic><topic>Image classification</topic><topic>Image databases</topic><topic>Image retrieval</topic><topic>Indexing</topic><topic>Noise reduction</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Changbo Yang</creatorcontrib><creatorcontrib>Ming Dong</creatorcontrib><creatorcontrib>Fotouhi, F.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Changbo Yang</au><au>Ming Dong</au><au>Fotouhi, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Image content annotation using Bayesian framework and complement components analysis</atitle><btitle>IEEE International Conference on Image Processing 2005</btitle><stitle>ICIP</stitle><date>2005</date><risdate>2005</risdate><volume>1</volume><spage>I</spage><epage>1193</epage><pages>I-1193</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9780780391345</isbn><isbn>0780391349</isbn><abstract>In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose to estimate the class conditional probabilities in the feature subspace constructed by complement components analysis (CCA). We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2005.1529970</doi></addata></record> |
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| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_1529970 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Digital images Feature extraction Image analysis Image classification Image databases Image retrieval Indexing Noise reduction Training data |
| title | Image content annotation using Bayesian framework and complement components analysis |
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