基于图像分析的梅花品种识别研究
Species identification of Prunus mume based on image analysis.
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摘要: 针对梅花图像，提出了适合其颜色、形状、纹理3方面特征的描述方法。在颜色特征提取方面，通过直方图归类的结果，提出了适合描述梅花图像色彩特征的方法。改进了对形状特征进行描述的平坦度算法，在计算平坦度时只计算花朵区域，没有统计背景区域，使算法得到简化，并且不影响最终的效果。改进了灰度共生矩阵的计算方法，首先提取出花朵区域的最小外接长方形，以此作为新的图像的长和宽，降低灰度共生矩阵的计算量；然后再计算4个角度的灰度共生矩阵，累加相应的矩阵元素除以4求出4个矩阵的平均矩阵，作为参与运算的灰度共生矩阵；计算该矩阵的相关参数作为纹理特征的描述。最后对提取到的19个特征采用SVM分类器进行分类和识别。对 660幅梅花图像（每个品种60幅，11个品种）进行测试，330幅作为训练样本，另外330幅作为测试样本。实验结果表明，在对SVM分类器做交叉验证后，识别率可达到93.94%。该识别系统具有较高的识别准确率和稳定性，能够起到知识普及的作用，减轻专业人员的负担，增加梅花的鉴赏性。Abstract: Prunus mume was studied as research object in this paper. Image segmentation, feature extraction, classification, etc. were studied based on the image of P. mume. A P. mume species recognition system was automatically established based on these technologies. Different methods were proposed for describing the color, shape and texture features of P. mume respectively. With its color feature extraction, the method suitable for describing the color characteristics of P. mume image was proposed through the results of a histogram classification. Its flatness describing its shaped feature was improved. Only the flower region was calculated for its flatness whereas the background region was not included. The algorithm was simpler than the original method and the final result was not to be affected using the improved method. The method of calculating gray level concurrence matrix (GLCM) was improved. The flowers smallest external rectangular region was extracted firstly as the new length and width of the image before calculating GLCM. Then the matrixes were calculated for four angles. The corresponding matrix elements were cumulated and divided with the results by 4 The average matrix was then used for the new GLCM, and the related parameters of the new matrix were calculated as part of texture features. At last, the SVM (support vector machine) was used for classification and recognition. Experiments were conducted for 660 P. mume images (60 images each for 11 different types). Three hundred and thirty images were used for the training samples, and the rest of 330 images was used for the test samples. After cross validation being conducted for SVM classifier, the experimental results showed that the average recognition accuracy reached 93.94%. The system has high recognition accuracy and stability. It is important to propagate the P. mumue, reduce the burden of professionals and increase the appreciation of P. mume.
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