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Journal of Electronic Imaging

Hierarchical abstract semantic model for image classification
Author(s): Zhipeng Ye; Peng Liu; Wei Zhao; Xianglong Tang
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Paper 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.

Paper Details

Date Published: 6 October 2015
PDF: 10 pages
J. Electron. Imag. 24(5) 053022 doi: 10.1117/1.JEI.24.5.053022
Published in: Journal of Electronic Imaging Volume 24, Issue 5
Show Author Affiliations
Zhipeng Ye, Harbin Institute of Technology (China)
Peng Liu, Harbin Institute of Technology (China)
Wei Zhao, Harbin Institute of Technology (China)
Xianglong Tang, Harbin Institute of Technology (China)

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