图像分类卷积神经网络的特征选择模型压缩方法

深度卷积神经网络(convolutional neural networks, CNN)作为特征提取器(feature extractor, CNN--FE)已被广泛应用于许多领域并获得显著成功. 根据研究评测可知CNN--FE具有大量参数, 这大大限制了CNN--FE在如智能手机这样的内存有限的设备上的应用. 本文以AlexNet卷积神经网络特征提取器为研究对象, 面向图像分类问题, 在保持图像分类性能几乎不变的情况下减少CNN--FE模型参数量. 通过对AlexNet各层参数分布的详细分析, 作者发现其全连接层包含了大约99%的模型参数, 在图像分类类别较少的情况, AlexNet提取的特征存在冗余. 因此, 将CNN--FE模型压缩问题转化为深度特征选择问题, 联合考虑分类准确率和压缩率, 本文提出了一种新的基于互信息量的特征选择方法, 实现CNN--FE模型压缩. 在公开场景分类数据库以及自建的无线胶囊内窥镜(wireless capsule endoscope, WCE)气泡图片数据库上进行图像分类实验. 结果表明本文提出的CNN--FE模型压缩方法减少了约83%的AlexNet模型参数且其分类准确率几乎保持不变.

Convolutional neural networks model compression based on feature selection for image classification

Deep convolutional neural networks (CNN) feature extractor (CNN--FE) has been widely applied in many applications and achieved great success. However, evaluating shows that the CNN--FE holds abundant parameters which largely limits its applications on memory-limited platforms, such as smartphones. This study makes an effort to trim the well-known CNN--FEs, AlexNet, to reduce its parameters meanwhile the image classification performance almost remains unchanged. This task is considered as a CNN--FE model compression problem. Through carefully analyzing the parameter distribution of AlexNet, we find about 99% of parameters are in its fully connected layer but the deep features are redundant for image classification tasks with small number of categories. Moreover, we propose to convert the CNN--FE model compression problem into a feature selection problem. Specifically, a feature selection method, which is based on mutual information and a novel criterion related to the classification accuracy and the compression ratio, has been proposed. Image classification experiments on a public scene categories database and our self-built wireless capsule endoscope (WCE) bubble dataset show that our proposed CNN--FE model compression method reduces more than 83% size of the AlexNet while almost maintaining the classification accuracy.