Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/104120
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Type: Journal article
Title: A graph-embedding approach to hierarchical visual word mergence
Author: Wang, L.
Liu, L.
Zhou, L.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(2):308-320
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2017
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Lei Wang, Lingqiao Liu and Luping Zhou
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.
Keywords: Clustering methods; computer vision; object recognition; supervised learning
Rights: © 2016 IEEE
DOI: 10.1109/TNNLS.2015.2509062
Grant ID: http://purl.org/au-research/grants/arc/LP0991757
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Computer Science publications

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