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Hierarchical abstract semantic model for image classification

[+] Author Affiliations
Zhipeng Ye, Peng Liu, Wei Zhao, Xianglong Tang

Harbin Institute of Technology, Pattern Recognition and Intelligent System Research Center, School of Computer Science and Technology, 92 West Dazhi Street, Harbin 150001, China

J. Electron. Imaging. 24(5), 053022 (Oct 06, 2015). doi:10.1117/1.JEI.24.5.053022
History: Received May 20, 2015; Accepted August 21, 2015
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Abstract.  Semantic gap limits the performance of bag-of-visual-words. To deal with this problem, a hierarchical abstract semantics method that builds abstract semantic layers, generates semantic visual vocabularies, measures semantic gap, and constructs classifiers using the Adaboost strategy is proposed. First, abstract semantic layers are proposed to narrow the semantic gap between visual features and their interpretation. Then semantic visual words are extracted as features to train semantic classifiers. One popular form of measurement is used to quantify the semantic gap. The Adaboost training strategy is used to combine weak classifiers into strong ones to further improve performance. For a testing image, the category is estimated layer-by-layer. Corresponding abstract hierarchical structures for popular datasets, including Caltech-101 and MSRC, are proposed for evaluation. The experimental results show that the proposed method is capable of narrowing semantic gaps effectively and performs better than other categorization methods.

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Citation

Zhipeng Ye ; Peng Liu ; Wei Zhao and Xianglong Tang
"Hierarchical abstract semantic model for image classification", J. Electron. Imaging. 24(5), 053022 (Oct 06, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053022


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