基于深度学习的复杂背景图像分类方法研究

复杂背景图像受背景干扰后不易被识别。针对这一问题,文中提出了基于前景分割机制的卷积神经网络图像分类方法。采用全卷积神经网络对图像前景区域进行自动分割,通过图像中前景区域周围的最小边界框对其进行定位。对于定位的前景区域,构建卷积神经网络对其进行处理以区分不同的类别,从而实现复杂背景图像的分类。将提出方法在公开数据集中提取的单一背景和复杂背景图像数据集上进行对比实验,并使用迁移学习与数据增强等方法优化模型。实验结果表明,所提方法使用前景区域分割相比于仅分类CNN具有更高的准确度,且复杂背景图像上的准确度提升幅度要远大于单一背景图像。该结果说明引入前景区域分割对于复杂背景图像分类模型准确度的提升具有一定帮助,能够显著前景区域特征并减少背景因素的干扰。

Research on Complex Background Image Classification Method Based on Deep Learning

To solve the problem that the complex background image cannot be easily recognized by background interference, a convolutional neural network image classification method based on foreground region segmentation mechanism is proposed. The foreground region of image is automatically segmented by using the full convolutional neural network, and is located by the minimum bounding box around it. A convolutional neural network is constructed to distinguish different foreground region categories, thereby realizing the classification of the complex background image. The proposed method is used to perform contrast experiments on simply and complex background image datasets extracted from the public dataset. Some effective methods, such as transfer learning and data augmentation, are used for model optimization. The experimental results show that the proposed method has higher accuracy than only classification CNN on both datasets, and the extent of model accuracy improvement on the complex background image is much larger than that on the simple background image. These results prove that the introduction of foreground region segmentation can improve the accuracy of complex background image classification model, highlight the characteriskics of the foreground region and reduce the interference of background factors.