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Received:February 08, 2020 Revised:March 03, 2020
Received:February 08, 2020 Revised:March 03, 2020
中文摘要: 有效识别图像或视频中人物的不同群体, 是进行图像智能分析的重要环节, 归根结底是研究如何获取图像中的“有效特征”. 本文以卷积神经网络模型为基础模型, 提出多模型融合卷积神经网络的方法, 利用ImageNet训练得到的模型参与本文神经网络模型的权值初始化, 在有效节省时间和计算资源成本的前提下获取更多有效的特征. 实验结果证明, 本模型对于自然场景中的个体分类中成年男性、成年女性、儿童识别准确率可以保持在85%左右, 提高了人物群体分类的准确度和可靠度.
Abstract:Effectively identifying the different group of human in an image or video is an important part of intelligent image analysis. It is how to obtain “effective features” in the image. Based on the convolution neural network model, this study proposes a multi-model fusion convolution neural network method. The model trained by ImageNet participates in the initialization of the weights of the neural network model, achieves more effective features on the premise of effectively saving time and resource calculating costs. Experiments prove that the model can maintain the recognition accuracy of adult males, adult females, and children in natural scenes at about 85%, which improves the accuracy and reliability of group classification.
keywords: image analysis efficient feature Convolution Neural Network (CNN) multi-model-integrated group classification
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基金项目:国家自然科学基金(61375122); 广东省创新强校特色创新类项目(201712009QX)
引用文本：
郎波,张娜,段新新.基于融合机制的多模型神经网络人物群体分类模型.计算机系统应用,2020,29(8):127-134
LANG Bo,ZHANG Na,DUAN Xin-Xin.Human Group Classification Model Based on Multi-Model-Integrated CNN.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):127-134
郎波,张娜,段新新.基于融合机制的多模型神经网络人物群体分类模型.计算机系统应用,2020,29(8):127-134
LANG Bo,ZHANG Na,DUAN Xin-Xin.Human Group Classification Model Based on Multi-Model-Integrated CNN.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):127-134
