DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Jaesik | ko |
dc.contributor.author | Choi, Jinho | ko |
dc.contributor.author | Yoo, Chang-Dong | ko |
dc.date.accessioned | 2023-11-08T08:01:01Z | - |
dc.date.available | 2023-11-08T08:01:01Z | - |
dc.date.created | 2023-11-08 | - |
dc.date.issued | 2014-10 | - |
dc.identifier.citation | 2014 IEEE International Conference on Image Processing, ICIP 2014, pp.155 - 159 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | http://hdl.handle.net/10203/314431 | - |
dc.description.abstract | This paper proposes a hierarchical-structured discriminative dictionary learning algorithm for image classification. Hierarchical structure of the overall dictionary is learned such that the upper-level dictionaries are specific in representing patterns common across a wide set of class images while lower-level dictionaries are specific in representing patterns localized to a narrow set of class images. Therefore the root dictionary can represent patterns common to all classes, while the leaf dictionaries can represent patterns specific only to a single distinct class. The learned dictionary is efficient in its use of the bases, and leads to a more discriminative representation than that led by previous dictionaries which is devoid of any structure and contains redundant bases. This hierarchical-structured dictionary is learned by solving a constraint optimization problem that minimized reconstruction error of a given image while using dictionaries in the hierarchical structure pertaining only to the class of the image. Sparse representation is pursued in addition, and it acts a regularizer to improve generalization. The representation is as distinct as the paths to each of the class in the hierarchical structure are divergent. To evaluate the effectness of the hierarchical-structured dictionary, classification is performed on three benchmark datasets: Extended Yale B database, Caltech 101 and Caltech 256 dataset, and based on a common features, the proposed algorithm performs better than other state-of-the-art dictionary learning algorithms. | - |
dc.language | English | - |
dc.publisher | IEEE Signal Processing Society | - |
dc.title | A hierarchical-structured dictionary learning for image classification | - |
dc.type | Conference | - |
dc.identifier.wosid | 000370063600032 | - |
dc.identifier.scopusid | 2-s2.0-84949928830 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 155 | - |
dc.citation.endingpage | 159 | - |
dc.citation.publicationname | 2014 IEEE International Conference on Image Processing, ICIP 2014 | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Paris | - |
dc.identifier.doi | 10.1109/ICIP.2014.7025030 | - |
dc.contributor.localauthor | Yoo, Chang-Dong | - |
dc.contributor.nonIdAuthor | Yoon, Jaesik | - |
dc.contributor.nonIdAuthor | Choi, Jinho | - |
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