Abstract:

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.

Key words: deep learning, convolutional neural network, image segmentation, foreground region, complex background, image recognition