多感知兴趣区域特征融合的图像识别方法

针对自然图像识别过程中不同深度学习模型关注兴趣区域不同的现象,本文引入深度卷积神经网络融合机制,结合深度迁移学习方法,给出了一种基于多感知兴趣区域特征融合的图像识别方法.本文将迁移学习方法引入牛津大学视觉组网络模型(visual geometry group network,VGGNet)和残差网络模型(residua...

Image recognition method based on multi-perceptual interest region feature fusion

This paper presents the deep convolution neural network fusion mechanism and proposes an image recognition method based on multi-perceptual interest region feature fusion in combination with the deep-migration learning method. This is to solve the problem of different deep-learning models used on different interest regions when they recognize a natural image. The migration learning method is applied to the convolution neural net architectures, namely VGG and ResNet networks. Then, through the visualization of the heat map and the features of single classification model, a conclusion is drawn that the characteristic regions associated with different network models are different. Based on this, the methods of feature splicing, feature fusion and splicing, and fusion voting systems are designed to fuse different model features, obtaining three new fusion models. The experimental results show that the recognition accuracy of this method on Kaggle dataset is higher than that of VGG-16, VGG-19, ResNet-50, and DenseNet-201 models.