Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/104120
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
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 |
Published version: | http://dx.doi.org/10.1109/tnnls.2015.2509062 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.