ABSTRACT

The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the auto-encoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion.

摘要

卷积神经网络（CNN）的卷积层和完全连接层的特征的组合提供了一种有效的方法来提高犯罪现场调查（CSI）图像分类的性能。但是，在现有工作中，由于特征融合的权重在训练阶段之后没有变化，因此可能会产生不准确的图像特征，从而影响分类结果。为了解决这个问题，本文提出了一种基于自动编码器的自适应特征融合方法，以提高分类精度。该方法包括以下步骤：首先，通过转移学习来训练CNN模型。接下来，分别提取卷积层和完全连接层的特征。然后将这些提取的特征传递到自动编码器中，以使用Softmax归一化进行进一步学习，以获得用于执行最终分类的自适应权重。实验表明，与固定权重特征融合相比，该方法具有更高的CSI图像分类性能。