基于多模型特征与精简注意力融合的图像分类

为了提高图像分类性能,本文提出一种多模型特征和注意力模块融合的图像分类算法(image classification algorithm based on Multi-model Feature and Reduced Attention fusion,MFRA).通过多模型特征融合,使网络学习输入图像不同层次的特征,增加特征互补性,提高特征提取能力;通过加入注意力模块,使网络更关注有目标的区域,降低无关的背景干扰信息.本文算法在Cifar-10,Cifar-100,Caltech-101这3个公开数据集上的大量实验对比,验证了其有效性.与现有算法对比,本文算法的分类性能有较为明显的提升.

Image Classification Based on Multi-Model Feature and Reduced Attention Fusion

To improve the performance of image classification, this paper proposes an image classification algorithm based on the fusion of Multi-model Feature and Reduced Attention (MFRA). Through multi-model feature fusion, the network can learn the features of different levels of input images, increase the complementarity of features and improve the ability of feature extraction. The introduction of the attention module makes the network pay more attention to the target area and reduces the irrelevant background interference information. In this paper, the effectiveness of the algorithm is verified by a large number of experimental comparisons on three public datasets, Cifar-10, Cifar-100 and Caltech-101. The classification performance of the proposed algorithm is significantly improved as compared with existing algorithms.