基于小波变换和改进KPCA的奶牛个体识别研究

Research of individual dairy cattle recognition based on wavelet transform and improved KPCA

为加快畜牧业现代化程度,克服传统方法中奶牛个体识别正确率低的缺陷,针对奶牛个体纹理特征,对传统KPCA(核主成分分析)方法从降低协方差矩阵维数和引入类别信息两个角度进行改进,并与小波变换进行结合,应用于奶牛个体识别领域。首先对归一化后的奶牛图像进行一层小波分解得到4个分量子图,然后对各子图利用改进的KPCA进行特征提取并引入加权策略融合,最后构造出多类SVM分类器进行学习分类。将预先采集的20头奶牛个体的视频数据转化成图片序列并选取20 000张组成实验数据集,通过多组对比实验对小波融合系数、融合向量组数、特征维数三个重要参数进行设定,然后利用不同算法进行奶牛个体识别实验。结果表明,提出方法在识别正确率达到96.31%时,仅用了4.20 s,较其他算法具有明显优势,可以有效地应用到奶牛个体识别领域,兼具高性能、低成本的优势。

To speed up the modernization of stockbreeding and overcome the defects of the low accuracy of individual dairy cattle recognition with traditional methods,the traditional KPCA( kernel principal component analysis) method was improved from two angles of reducing the covariance matrix dimension and introducing category information. The research of combining wavelet transform with improved KPCA was applied for recognition dairy cattle based on the texture feature. Firstly,the normalized dairy cattle image was decomposed by wavelet transform to obtain four subgraphs. Then an improved KPCA algorithm was used for feature extraction of each sub-graph and the feature matrix was obtained by weighting the feature components. Finally,multi-class SVM algorithm was built for training and classification. The pre-collected 20 dairy cattle's videos were converted into image sequence and 20 000 images were chosen to form experiment data sets. Through several groups of experiments for three important parameters of the wavelet fusion weights,the number of fusion vector groups and the feature dimension,values were set and then experiments of individual dairy cattle recognition were performed using different algorithms. The results showed that it took only 4. 20 s for the proposed method reach the accuracy of 96. 31% ,which has obvious advantages over other algorithms.It can be appropriately applied to the field of dairy cattle individual recognition with high performance and low cost.