6 October 2015 Hierarchical abstract semantic model for image classification
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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.
© 2015 SPIE and IS&T
Zhipeng Ye, Zhipeng Ye, Peng Liu, Peng Liu, Wei Zhao, Wei Zhao, Xianglong Tang, Xianglong Tang, } "Hierarchical abstract semantic model for image classification," Journal of Electronic Imaging 24(5), 053022 (6 October 2015). https://doi.org/10.1117/1.JEI.24.5.053022 . Submission:

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