dc.contributor.authorGao, Shenghua
dc.contributor.authorTsang, Ivor Wai-Hung
dc.contributor.authorMa, Yi
dc.date.accessioned2016-08-16T08:22:29Z
dc.date.available2016-08-16T08:22:29Z
dc.date.issued2014
dc.identifier.citationGao, S., Tsang, I. W.-H., & Ma, Y. (2014). Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization. IEEE Transactions on Image Processing, 23(2), 623-634.en_US
dc.identifier.issn1057-7149en_US
dc.identifier.urihttp://hdl.handle.net/10220/41143
dc.description.abstractThis paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.en_US
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Transactions on Image Processingen_US
dc.rights© 2013 IEEE.en_US
dc.subjectClass-specific dictionaryen_US
dc.subjectShared dictionaryen_US
dc.titleLearning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorizationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TIP.2013.2290593


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