基于智能视觉的小差异行为特征分类

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英文篇名：Classification of Small Difference Behavior Characteristics Based on Intelligent Vision 作者：陈威 ; 刘艳 ; 雷庆 英文作者：CHEN Wei;LIU Yan;LEI Qing;College of Computer Science and Technology,Huaqiao University; 关键词：智能视觉 ; 小差异行为 ; 特征提取 ; 预处理 ; 分类 英文关键词：Intelligent vision;;Small difference behavior;;Feature extraction;;Preprocessing;;Classification 中文刊名：JSJA 英文刊名：Computer Science 机构：华侨大学计算机科学与技术学院; 出版日期：2019-03-15 出版单位：计算机科学 年：2019 期：v.46 基金：福建省自然科学基金项目(2017J01110)资助 语种：中文; 页：JSJA201903044 页数：5 CN：03 ISSN：50-1075/TP 分类号：304-308

摘要

针对传统差异行为特征分类方法难以对小差异行为进行有效识别且分类精度低等缺陷,提出了基于智能视觉的小差异行为特征分类方法。首先采用免疫多Agent方法对小差异行为进行特征提取,对获取的图像集合实施免疫多Agent操作,分析人物轻微形变的小差异行为,获取特征提取集;然后采用视频帧图像阵列检测方法对特征提取集实施像素灰度预处理,通过构建视频帧图像阵列,跟踪识别初始化学习得到灰度像素值,获取较优的小差异行为特征集;最后基于多衡量标准的小差异行为特征分类方法,对较优特征集实施分割操作,采用各个特征子集与衡量标准对比的方式,获取最优小差异行为特征分类结果。实验结果表明,所提方法提高了小差异行为特征的分类精度,且具有较高的工作效率。
        In view of the shortcomings of traditional difference behavior feature classification methods,such as ineffective recognition of small difference behavior and low classification accuracy,a classification method of small difference behavior feature based on intelligent vision was put forward.Firstly,with the immune multi-agent method,the features of small difference behavior are extracted to conduct the immune multi-agent operation for the acquired image set and to analyze the small difference behavior of the slight deformation of the characters to obtain the feature extraction set.Then,the method of video frame image array detection is used to pre-process the gray level of the pixels in the feature extraction set.By constructing the video frame image array,the gray level pixel value is obtained by tracking and recognition initialization learning,and the better small difference behavior feature set is obtained.Finally,the multi-criteria small difference behavior feature classification method is used to segment the better feature set,and the best small difference behavior feature classification results are obtained by contrasting each feature subset with the measurement criteria.The experimental results show that the proposed method improves the classification accuracy of small difference behavior features with a high efficiency.

引文

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