利用GLCM和颜色特征提取对奶牛类型进行CBIR分类

T. Sutojo, D. Setiadi, Pungky Septiana Tirajani, C. A. Sari, E. H. Rachmawanto
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引用次数: 42

摘要

牛是对人类有很多好处的动物之一。奶牛的种类根据效益有不同,如奶牛、肉牛、工人牛等。养牛应根据公众的需要进行调整。对不同种类牛的了解不足会降低养殖牛的效益。基于内容的图像检索(Content Based Image Retrieval, CBIR)可以用来帮助识别或了解奶牛的类型。本研究提出的方法的第一步是通过改变背景颜色、调整大小和转换颜色空间进行预处理。颜色特征提取计算每个颜色分量的颜色强度的平均值和标准差。然后利用灰度共生矩阵(GLCM)提取纹理特征,分别在0°、45°、90°和135°角度寻找对比度、能量、相关性、均匀性和熵,平均值为1。以6个颜色特征和5个纹理特征作为属性,利用欧几里得距离进行计算,从而知道图像之间的相似度。使用的牛类型包括利穆赞(Limousin)、西蒙塔尔(Simental)、布兰格斯(Brangus)、Peranakan Ongole (PO)和Frisien Holstein (FH)。有100个训练图像和20个测试图像。为了衡量所提出的CBIR的准确性,使用了混淆矩阵。测量结果表明,该方法的准确度为95%,精密度和召回率均为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CBIR for classification of cow types using GLCM and color features extraction
Cow is one of the animals that have many benefits for humans. There are various types of cows based on benefits such as dairy cows, beef cattle, worker cattle, and others. Cattle breeding should be tailored to the needs of the public. Less knowledge about different types of cattle can reduce the benefits of farmed cattle. Content Based Image Retrieval (CBIR) can be applied to help the problem of distinguishing or knowing the type of cow. The first step of the method proposed in this research is preprocessing by changing the background color, resizing and conversion of color space. Color feature extraction calculates the average and standard deviation of the color intensity of each color component. Next extract the texture feature using Gray Level Cooccurrence Matrix (GLCM) to look for contrast, energy, correlation, homogeneity and entropy at each angle 0°, 45°, 90° and 135° with a mean of 1 averaged. Six color features and five texture features are used as attributes to perform calculations with Euclidean Distance, so it can be known the similarity between images. Cattle types used include Limousin, Simental, Brangus, Peranakan Ongole (PO), and Frisien Holstein (FH). With 100 training images and 20 test images. To measure the accuracy of the proposed CBIR is used Confusion Matrix. Based on the measurement results obtained accuracy of 95% while the precision and recall obtained 100%.
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