Tongue Localization Method Based on Cascade Classifier
DOI: 10.23977/jaip.2020.030104 | Downloads: 13 | Views: 1638
Chao Song 1
1 School of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210046, China
Corresponding AuthorChao Song
Traditional Chinese Medicine (TCM) verifies that tongue images are closely related to the health of the human organs and tongues’ visual features can provide valuable clues for disease diagnosis. How to locate the tongue region is an important step in the intelligent development of the TCM tongue diagnosis, because the effective removal of interference information outside the tongue can effectively enhance the extraction of tongue features. This paper proposes a cascade classifier based on Local Binary Pattern (LBP) feature to locate and segment the tongue body, which effectively improves the classification accuracy of the tongue feature.
KEYWORDSTongue localization, Cascade classifier, Local Binary Pattern
CITE THIS PAPER
Chao Song. Tongue Localization Method Based on Cascade Classifier. Journal of Artificial Intelligence Practice (2020) Vol. 3: 13-21. DOI: http://dx.doi.org/10.23977/jaip.2020.030104.
 Wei Yuke, Fan Peng, Zeng Gui. Application of improved GrabCut method in tongue diagnosis system [J]. Sensors and Microsystems, 2014, 33 (10): 157-160.
 Jiang Shuo, Hu Jie, Xia Chunming, et al. Tongue image segmentation method based on Otsu threshold method and morphological adaptive correction [J]. High Technology Letters, 2017 (2).
 Chen Shanchao, Fu Hongguang, Wang Ying. Application of an improved graph theory segmentation method in tongue image segmentation [J]. Computer Engineering and Applications, 2012, 48 (5): 201-203.
 Boykov Y Y. Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images [C]// Proc Eighth IEEE International Conference on Comput Vis. IEEE Computer Society, 2001.
 Rother C. GrabCut: Interactive foreground extraction using iterated graph cuts [J]. Proceedings of Siggraph, 2004, 23.
 Agarwala A, Dontcheva M, Agrawala M, et al. Interactive Digital Photomontage [J]. ACM Transactions on Graphics, 2004, 23 (3).
 Zhou Liangfen, He Jiannong. Improved image segmentation algorithm based on GrabCut [J]. Journal of Computer Applications, 2013, 33 (01):49-52.
 Xu Qiuping, Guo Min, Wang Yarong. Fast image segmentation algorithm based on multi-scale analysis and graph cutting [J]. Application Research of Computers, 2009, 26 (10): 3989-3991.
 Han S, Tao W, Wang D, et al. Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor [J]. Image Processing IEEE Transactions on, 2009, 18 (10): p.2289-2302.
 Shanmugavadivu P, Thenmozhi G. Detection of microcalcification in mammogram images using semi-automated texture based Grabcut Segmentation [C]// International Conference on Emerging Trends in Science, Engineering & Technology. IEEE, 2012.
 Zhang Ling, Qin Jian. Tongue image segmentation method based on gray projection and automatic threshold selection [J]. Chinese Tissue Engineering Research and Clinical Rehabilitation, 2010, 14 (09): 1638-1641.
 Zhang Zhishun, Liu Yong. Tongue extraction algorithm based on dynamic threshold and modified model[J]. Computer and Modernization, 2014 (11): 49-52.
 Huang Qian, Yang Wenliang, Gu Jiefeng. An improved image segmentation method based on graph theory[J]. Science Technology and Engineering, 2009, 9(13): 3652-3656+3671.
 Dongcai S. Efficient Graph based image Segmentation [J]. image processing.
 P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition. CVPR 2001, IEEE Comput. Soc, 2001: pp. I-511-I–518.
 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. (2015) 1–14.