Saliency-weighted holistic scene text recognition for unseen place categorization
An improvement in framework for unseen place categorization using scene text is proposed. Category score calculation using visual saliency weighting method is proposed to cope with problem of different importance of word locations on scene images. Additionally, a HOG feature extraction using sliding...
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| Main Authors: | , , |
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| 格式: | Conference Proceeding |
| 出版: |
2018
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| 主題: | |
| 在線閱讀: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904543396&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/53421 |
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| 機構: | Chiang Mai University |
| 總結: | An improvement in framework for unseen place categorization using scene text is proposed. Category score calculation using visual saliency weighting method is proposed to cope with problem of different importance of word locations on scene images. Additionally, a HOG feature extraction using sliding window is proposed to obtain better holistic word recognition on scene images. As the result, the proposed method outperforms PHOG baseline in unseen place categorization with greater than 10 % improvement in the accuracy. © 2014 IEEE. |
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