Semantic Home Photo Categorization
A semantic categorization method for generic home photo is proposed. The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate t...
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| Veröffentlicht in: | IEEE transactions on circuits and systems for video technology 2007-03, Vol.17 (3), p.324-335 |
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| container_title | IEEE transactions on circuits and systems for video technology |
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| creator | Yang, Seungji Kim, Sang-Kyun Man Ro, Yong |
| description | A semantic categorization method for generic home photo is proposed. The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate to detect local and global photo semantics in a feed-forward way. The first layer aims to predict likelihood of predefined local photo semantics based on camera metadata and regional low-level visual features. In the second layer, one or more global photo semantics is detected based on the likelihood. To construct classifiers producing a posterior probability, we use a parametric model to fit the output of SVM classifiers to posterior probability. A concept merging process based on a set of semantic-confidence maps is also presented to cope with selecting more likelihood photo semantics on spatially overlapping local regions. Experiment was performed with 3086 photos that come from MPEG-7 visual core experiment two official databases. Results showed that the proposed method would much better capture multiple semantic meanings of home photos, compared to other similar technologies |
| doi_str_mv | 10.1109/TCSVT.2007.890829 |
| format | Article |
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The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate to detect local and global photo semantics in a feed-forward way. The first layer aims to predict likelihood of predefined local photo semantics based on camera metadata and regional low-level visual features. In the second layer, one or more global photo semantics is detected based on the likelihood. To construct classifiers producing a posterior probability, we use a parametric model to fit the output of SVM classifiers to posterior probability. A concept merging process based on a set of semantic-confidence maps is also presented to cope with selecting more likelihood photo semantics on spatially overlapping local regions. Experiment was performed with 3086 photos that come from MPEG-7 visual core experiment two official databases. Results showed that the proposed method would much better capture multiple semantic meanings of home photos, compared to other similar technologies</description><subject>Camera metadata</subject><subject>Cameras</subject><subject>Classifiers</subject><subject>Computer vision</subject><subject>Feedforward systems</subject><subject>image classification</subject><subject>Mathematical models</subject><subject>Merging</subject><subject>Metadata</subject><subject>MPEG 7 Standard</subject><subject>Parametric statistics</subject><subject>photo album</subject><subject>Semantics</subject><subject>Spatial databases</subject><subject>support vector machine</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Visual</subject><subject>Visual databases</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90D1PwzAQgGELgUQp_ADEUjHAlHLnj9QeUQQUqRJIjVgt13YgVRMXOx3g15MSxMDA5BueO8kvIecIU0RQN2WxfCmnFGA2lQokVQdkhELIjFIQh_0MAjNJURyTk5TWAMgln43I5dI3pu1qO5mHxk-e30IXJoXp_GuI9afp6tCekqPKbJI_-3nHpLy_K4t5tnh6eCxuF5llQnSZrMzMuNxShZZaBR6McV66ykq_Es5RpyRlFnFVcXDcecsMCCYMcGArysbkeji7jeF951OnmzpZv9mY1odd0lJCLlSe7-XVv5JxQRmleQ8v_8B12MW2_4RWSPsYAlmPcEA2hpSir_Q21o2JHxpB79vq77Z631YPbfudi2Gn9t7_eo4oKZfsC6widB4</recordid><startdate>20070301</startdate><enddate>20070301</enddate><creator>Yang, Seungji</creator><creator>Kim, Sang-Kyun</creator><creator>Man Ro, Yong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate to detect local and global photo semantics in a feed-forward way. The first layer aims to predict likelihood of predefined local photo semantics based on camera metadata and regional low-level visual features. In the second layer, one or more global photo semantics is detected based on the likelihood. To construct classifiers producing a posterior probability, we use a parametric model to fit the output of SVM classifiers to posterior probability. A concept merging process based on a set of semantic-confidence maps is also presented to cope with selecting more likelihood photo semantics on spatially overlapping local regions. Experiment was performed with 3086 photos that come from MPEG-7 visual core experiment two official databases. 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| source | IEEE Electronic Library (IEL) |
| subjects | Camera metadata Cameras Classifiers Computer vision Feedforward systems image classification Mathematical models Merging Metadata MPEG 7 Standard Parametric statistics photo album Semantics Spatial databases support vector machine Support vector machine classification Support vector machines Visual Visual databases |
| title | Semantic Home Photo Categorization |
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