For image classification, convolutional neural networks (CNNs) have the advantage of being able to convolve directly with image pixels and extract image features from image pixels. This approach is closer to the treatment of the human brain's visual system. However, up to now, it is impossible to achieve 100% classification accuracy regardless of any kind of convolutional neural network models. At the same time, we also find that sometimes the background of images affects the recognition of neural networks, and removing the background of images can improve their performance of object recognition. Therefore, we design a model named DOG based on CNN to remove the background of images (such as selfies, animals, flowers, etc.) for improving the performance of object recognition. Because of the scarce of samples, we further integrate DOG with DCGAN to further improve the performance of object recognition. Our experimental results show the effectiveness of DOG.
- Object recognition
- Semantic segmentation