CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
杨萌林1,2; 张文生1,2
Source Publication计算机科学与探索


Other Abstract

This paper proposed a new image classification algorithm based on the classification activation map (CAM). CAM is a kind of feature map with high-level semantics, which could reflect the response of each area in the image to the classification. The CAM can be as a visualized measure by using simple post-processing method. However, under the supervision of classification labels, the model tends to focus on the local most discriminative areas while ignoring the integrity of the target, resulting in sparse and incontinuity problems, but the suppressed areas can also provide semantic information. In addition, the research of CAM only stayed in the visualization in the classification framework before, without further exploration and utilization. In this paper, the CAM is further explored to enhance the classification. In order to achieve such a goal, this paper first designs an automatic weighted multi-scale feature learning method to mine more discriminative information. Furthermore, the multi-scale feature is combined with the CAM, and a method for directly generating CAM is proposed. The method can embed the CAM into the network to form an end-to-end structure, thus achieving classification performance enhancement. With ResNet as the backbone, this paper proposes an image classification model ResNet-CE. A large number of experiments were performed on three public datasets CIFAR10, CIFAR100 and STL10. The experiments show that: the classification performance of ResNet-CE on these three datasets reaches 5.73%, 23.85% and 15.91% error rate; and compared with the benchmark ResNet, the error rate is obviously lower which decrease by 0.23%, 3.56% and 7.96%, respectively. In addition, the performance of the proposal model is better than most of the current classification networks. The proposal based on CAM enhancement can be easily transferred to the off-the-shelf networks. At the same time, the algorithm can visualize and interpret the judgment of the model. The proposal has certain application value and significance in many applications, such as disease recognition in medical images and target recognition in remote sensing images.

Keyword图像分类 分类激活图 多尺度 可视化 可解释性
Indexed ByCSCD
Funding ProjectNational Natural Science Foundation of China[U1636220]
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Document Type期刊论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
杨萌林,张文生. 分类激活图增强的图像分类算法[J]. 计算机科学与探索,2019(00):00.
APA 杨萌林,&张文生.(2019).分类激活图增强的图像分类算法.计算机科学与探索(00),00.
MLA 杨萌林,et al."分类激活图增强的图像分类算法".计算机科学与探索 .00(2019):00.
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