Semantic home photo categorization

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2007-03
Language
English
Article Type
Article
Keywords

IMAGE CLASSIFICATION; REGION TEMPLATES; RETRIEVAL; MODELS; VIDEO

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.17, pp.324 - 335

ISSN
1051-8215
DOI
10.1109/TCSVT.2007.890829
URI
http://hdl.handle.net/10203/20940
Appears in Collection
EE-Journal Papers(저널논문)
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