基于改进特征袋模型的奶牛识别算法

doi:10.11772/j.issn.1001-9081.2016.08.2346

计算机应用 ›› 2016, Vol. 36 ›› Issue (8): 2346-2351.DOI: 10.11772/j.issn.1001-9081.2016.08.2346

基于改进特征袋模型的奶牛识别算法

陈娟娟, 刘财兴, 高月芳, 梁云

华南农业大学数学与信息学院, 广州 510642

收稿日期:2016-01-27 修回日期:2016-03-17 出版日期:2016-08-10 发布日期:2016-08-10
通讯作者: 刘财兴
作者简介:陈娟娟(1991-),女,河南周口人,硕士研究生,CCF会员,主要研究方向:图形图像处理;刘财兴(1962-),男,广东韶关人,教授,CCF高级会员,主要研究方向:传感器网络、软件工程;高月芳(1979-),女,河南周口人,副教授,博士,主要研究方向:图形图像处理;梁云(1981-),女,山东临沂人,副教授,博士,主要研究方向:计算机视觉、图形图像处理。
基金资助:
国家自然科学基金资助项目（61202293）；广东省科技计划项目（2015A020209124）。

Cow recognition algorithm based on improved bag of feature model

CHEN Juanjuan, LIU Caixing, GAO Yuefang, LIANG Yun

College of Mathematics and Informatics, South China Agricultural University, Guangzhou Guangdong 510642, China

Received:2016-01-27 Revised:2016-03-17 Online:2016-08-10 Published:2016-08-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61202293), the Science and Technology Planning Project of Guangdong Province (2015A020209124).

摘要/Abstract

摘要： 针对特征袋（BOF）模型中存在特征计算耗时、识别精度低的不足，提出一种新的改进BOF模型以提高其目标识别的精度和效率，并将其应用于奶牛个体识别。该算法首先引入优化方向梯度直方图（HOG）特征对图像进行特征提取和描述，然后利用空间金字塔匹配原理（SPM）生成图像基于视觉词典的直方图表示，最后自定义直方图交叉核作为分类器核函数。该算法在项目组自行拍摄的数据集（包含15类奶牛、共7500张奶牛头部图像）上的实验结果表明，使用基于SPM的BOF模型将算法的识别率平均提高2个百分点；使用直方图交叉核相比使用高斯核将算法的识别率平均提高2.5个百分点；使用优化HOG特征，相比使用传统HOG特征将算法识别率平均提高21.3个百分点，运算效率为其1.68倍；相比使用尺度不变特征变换（SIFT）特征，在保证平均识别精度达95.3%的基础上，运算效率为其7.10倍。分析结果可知，该算法在奶牛个体识别领域具有较好的鲁棒性和实用性。

关键词: 特征袋模型, 图像识别, 梯度直方图特征, 空间金字塔匹配, 尺度不变特征变换特征

Abstract: Concerning the high time-consuming and low recognition accuracy of Bag of Feature (BOF) model, a new improved BOF model was proposed to improve the accuracy and efficiency of target recognition, and it was also applied to cow recognition. The optimized Histogram of Oriented Gradient (HOG) feature was introduced to feature extraction and description of the images; then the Spatial Pyramid Matching (SPM) principle was used to generate the histogram representation of images based on visual dictionary; finally, the histogram intersection kernel defined in this paper was used as the kernel function of the classifier. The experimental results on the data set in this paper (including 15 kinds of cows with 7500 images of cow heads) showed that the recognition rate of the algorithm was improved by an average of 2 percentage points by using the BOF model based on SPM; compared with Gauss kernel, the recognition rate of the algorithm was increased by an average of 2.5 percentage points by using the histogram intersection kernel; compared with traditional HOG feature, the recognition rate of the algorithm was improved by an average of 21.3 percentage points by using optimized HOG feature, and the computation efficiency of the algorithm was improved by an average of 1.68 times; compared with Scale Invariant Feature Transform (SIFT) feature, the computation efficiency of the algorithm was improved by an average of nearly 7.10 times as well as ensuring the average recognition accuracy reached 95.3%. Analysis results indicate that this algorithm has good robustness and practicability in cow individual recognition.

Key words: Bag of Feature(BOF)model, image recognition, Histogram of Oriented Gradient(HOG)feature, Spatial Pyramid Matching(SPM), Scale Invariant Feature Transform(SIFT)feature

中图分类号:

TP391.41

陈娟娟, 刘财兴, 高月芳, 梁云. 基于改进特征袋模型的奶牛识别算法[J]. 计算机应用, 2016, 36(8): 2346-2351.

CHEN Juanjuan, LIU Caixing, GAO Yuefang, LIANG Yun. Cow recognition algorithm based on improved bag of feature model[J]. Journal of Computer Applications, 2016, 36(8): 2346-2351.

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基于改进特征袋模型的奶牛识别算法

Cow recognition algorithm based on improved bag of feature model

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