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Author:

Zhuo, Li (Zhuo, Li.) (Scholars:卓力) | Zhang, Pei (Zhang, Pei.) | Cheng, Bo (Cheng, Bo.) | Li, Xiaoguang (Li, Xiaoguang.) | Zhang, Jing (Zhang, Jing.)

Indexed by:

CPCI-S

Abstract:

Content-Based Image Retrieval (CBIR) characterizes the image content by extracting visual features, and measures the similarity according to the distance between the two features. This paper adopts CBIR to perform automatic tongue color analysis of Traditional Chinese Medicine (TCM). Firstly, we extract the visual features of tongue images to be analyzed, especially the color features; and then retrieve the similar tongue images from the database, which have been labeled by TCM doctors in advance. Finally, statistical decision method is exploited based on the retrieval results to classify the tongue color. Experimental results show that the proposed method can achieve the classification accuracy of 87.85% and 88.54% respectively for the colors of tongue substance and tongue coating. The proposed method in this paper can provide a new means for the tongue color automatic analysis of TCM, and it is also a new application of CBIR.

Keyword:

Traditional Chinese Medical Tongue diagnosis statistical decision tongue image retrieval

Author Community:

  • [ 1 ] [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 2 ] [Zhang, Pei]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 3 ] [Cheng, Bo]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 4 ] [Li, Xiaoguang]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 5 ] [Zhang, Jing]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

Reprint Author's Address:

  • 卓力

    [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

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Source :

2014 13TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION (ICARCV)

ISSN: 2474-2953

Year: 2014

Page: 637-641

Language: English

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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