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|Title:||A graph-embedding approach to hierarchical visual word mergence|
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(2):308-320|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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|
|Appears in Collections:||Aurora harvest 3|
Computer Science publications
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