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http://dx.doi.org/10.6109/jkiice.2016.20.3.621

Middle Ear Disease Automatic Decision Scheme using HoG Descriptor  

Jung, Na-ra (Graduate School of Electrical Engineering and Computer Science, Chungbuk University)
Song, Jae-wook (Graduate School of Electrical Engineering and Computer Science, Chungbuk University)
Choi, Ho-Hyoung (Graduate School of Electrical Engineering and Computer Science, Chungbuk University)
Kang, Hyun-soo (Graduate School of Electrical Engineering and Computer Science, Chungbuk University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.3, 2016 , pp. 621-629 More about this Journal
Abstract
This paper presents a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HoG (histogram of oriented gradient) descriptor and the extracted features are learned by SVM (support vector machine) classifier. To obtain an input vector into SVM, an input image is resized to a predefined size and then the resized image is partitioned into 16 blocks each of which is partitioned into 4 sub-blocks (namely cell). Finally, the feature vector with 576 components is given by using HoG with 9 bins and it is used as SVM learning and classification. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the proposed method yields the precision of over 90% in decision.
Keywords
HoG; SVM; Middle ear Disease; Classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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