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https://hdl.handle.net/2440/104120
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dc.contributor.author | Wang, L. | - |
dc.contributor.author | Liu, L. | - |
dc.contributor.author | Zhou, L. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(2):308-320 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.issn | 2162-2388 | - |
dc.identifier.uri | http://hdl.handle.net/2440/104120 | - |
dc.description.abstract | Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various formulations. In this paper, we propose a unified hierarchical merging approach built upon the graph-embedding framework. Our approach is able to merge visual words for any scenario, where a preferred structure and an undesired structure are defined, and, therefore, can effectively attend to all kinds of requirements for the word-merging process. In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work and, thus, well maintain the state-of-the-art merging speed. To the best of our survey, the proposed approach is the first one that addresses the hierarchical visual word mergence in such a flexible and unified manner. As demonstrated, it can maintain excellent image classification performance even after a significant dimension reduction, and outperform all the existing comparable visual word-merging methods. In a broad sense, our work provides an open platform for applying, evaluating, and developing new criteria for hierarchical word-merging tasks. | - |
dc.description.statementofresponsibility | Lei Wang, Lingqiao Liu and Luping Zhou | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.rights | © 2016 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/tnnls.2015.2509062 | - |
dc.subject | Clustering methods; computer vision; object recognition; supervised learning | - |
dc.title | A graph-embedding approach to hierarchical visual word mergence | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TNNLS.2015.2509062 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/LP0991757 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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