Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/53421
Title: Saliency-weighted holistic scene text recognition for unseen place categorization
Authors: Phawis Thammasorn
Karn Patanukhom
Rapeeporn Pimup
Keywords: Computer Science
Issue Date: 1-Jan-2014
Abstract: 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904543396&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/53421
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.