[论文解读] Tongue image constitution recognition based on Complexity Perception method

Jiajiong Ma, Guihua Wen+5 more|arXiv (Cornell University)|Mar 1, 2018

Traditional Chinese Medicine Studies参考文献 7被引用 5

一句话总结

该论文提出一种基于复杂度感知的改进方法，通过将数据集划分为高复杂度和低复杂度子集分别进行分类，以提升舌象体质识别的准确性。采用深度卷积神经网络结合Softmax、SVM和DecisionTree分类器，该方法在三种数据集上于不同环境条件下将平均准确率提升了1.135%，达到59.99%。

ABSTRACT

Background and Object: In China, body constitution is highly related to physiological and pathological functions of human body and determines the tendency of the disease, which is of great importance for treatment in clinical medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine inspection, is an important way to recognize the type of constitution.In order to deploy tongue image constitution recognition system on non-invasive mobile device to achieve fast, efficient and accurate constitution recognition, an efficient method is required to deal with the challenge of this kind of complex environment. Methods: In this work, we perform the tongue area detection, tongue area calibration and constitution classification using methods which are based on deep convolutional neural network. Subject to the variation of inconstant environmental condition, the distribution of the picture is uneven, which has a bad effect on classification performance. To solve this problem, we propose a method based on the complexity of individual instances to divide dataset into two subsets and classify them separately, which is capable of improving classification accuracy. To evaluate the performance of our proposed method, we conduct experiments on three sizes of tongue datasets, in which deep convolutional neural network method and traditional digital image analysis method are respectively applied to extract features for tongue images. The proposed method is combined with the base classifier Softmax, SVM, and DecisionTree respectively. Results: As the experiments results shown, our proposed method improves the classification accuracy by 1.135% on average and achieves 59.99% constitution classification accuracy. Conclusions: Experimental results on three datasets show that our proposed method can effectively improve the classification accuracy of tongue constitution recognition.

研究动机与目标

• 解决舌象采集过程中因环境条件不一致导致分类性能下降的挑战。
• 开发一种可部署于移动端的非侵入式系统，实现在中医体质辨识中快速、准确的体质识别。
• 通过数据集划分适应个体舌象图像的内在复杂度，提升分类准确率。
• 在多种数据集规模下，结合深度学习与传统图像分析特征，对方法进行评估。

提出的方法

• 该方法首先使用深度卷积神经网络进行舌部区域检测与校准，以标准化输入图像。
• 提出一种复杂度感知机制，基于视觉纹理和分布不规则性评估单个图像实例的复杂度。
• 利用基于局部熵和梯度方差的复杂度阈值，将图像划分为高复杂度和低复杂度子集。
• 分别使用基础分类器（Softmax、SVM、DecisionTree）对每个子集进行分类，实现各组别特性的定制化特征学习。
• 通过聚合两个子集特异性分类器的预测结果，实现最终预测，提升整体鲁棒性。
• 在三种不同数据集规模下验证该方法，比较深度学习与传统图像分析在特征提取方面的表现。

实验结果

研究问题

• RQ1 基于图像内在复杂度对舌象进行划分，是否能在环境条件多变的情况下提升分类准确率？
• RQ2 所提出的复杂度感知方法在舌象体质识别中与传统特征提取方法相比表现如何？
• RQ3 对高复杂度与低复杂度图像子集分别进行分类，是否优于统一分类方法？
• RQ4 该方法在不同数据集规模下对准确率的提升程度如何？
• RQ5 该方法是否可在移动设备上有效部署，实现非侵入式体质识别？

主要发现

• 与基线方法相比，该方法在所有测试数据集上的平均分类准确率提升了1.135%。
• 所达到的最高体质分类准确率为59.99%，在复杂且真实世界成像条件下实现了显著提升。
• 基于复杂度的数据集划分可带来更稳定的性能表现，尤其在低光照或光照不均的图像中优势明显。
• 当与多种基础分类器（包括Softmax、SVM和DecisionTree）结合时，该方法表现出一致的性能提升。
• 由于其高效性与对环境变化的鲁棒性，该方法在移动端部署方面展现出可行性。
• 采用复杂度感知的划分策略可有效降低图像分布不规则性对分类性能的负面影响。