Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection
Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection
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DOI：
10.1016/j.meatsci.2009.10.030
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发表时间：
2010-04-01
期刊：
影响因子：
7.1
通讯作者：
Ward, Paddy
中科院分区：
文献类型：
--
作者：
Jackman, Patrick;Sun, Da-Wen;Ward, Paddy
开发了一种利用颜色和小波纹理特征区分不同等级猪肉和火鸡火腿的方法。原本为预测牛肉适口性而开发的图像分析方法被应用于快速识别火腿等级。通过对每个火腿获取50 - 94片高质量数字图像，有可能确定最能体现不同火腿等级差异的灰度。然后利用遗传算法找出了10个最佳的区分图像特征。利用这10个最佳图像特征，简单线性判别分析模型在校准集和验证集上对猪肉和火鸡都实现了100%的正确分类。(C) 2009爱思唯尔有限公司。保留所有权利。
A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. (C) 2009 Elsevier Ltd. All rights reserved.