基于Xception的细粒度图像分类

细粒度图像分类是对传统图像分类的子类进行更加细致的划分,实现对物体更为精细的识别,它是计算机视觉领域的一个极具挑战的研究方向。通过对现有的细粒度图像分类算法和Xception模型的分析,提出将Xception模型应用于细粒度图像分类任务。用ImageNet分类的预训练模型参数作为卷积层的初始化,然后对图像进行缩放、数据类型转换、数值归一化处理,以及对分类器参数随机初始化,最后对网络进行微调。在公开的细粒度图像库CUB200-2011、Flower102和Stanford Dogs上进行实验验证,得到的平均分类正确率为71.0%、89.9%和91.4%。实验结果表明Xception模型在细粒度图像分类上有很好的泛化能力。由于不需要物体标注框和部位标注点等额外人工标注信息,Xception模型用在细粒度图像分类上具有较好的通用性和鲁棒性。

Fine-grained image classification based on Xception

Fine-grained image classification is a more detailed division of the sub-categories of traditional image classification, which achieves a more sophisticated identification of objects. And it is a very challenging research in the field of computer vision. By analyzing the existing fine-grained image classification algorithm and Xception model, we propose to apply the Xception model to the fine-grained image classification task. Initialization of convolution layers uses pre-training model parameters of ImageNet classification. Then we resize images, transform data type, normalize value, and randomly initialize classifier. Finally, the network is fine-tuned. Our method obtains 71.0%, 89.9% and 91.4% per-image accuracy on the CUB200-2011, Flower102 and Stanford Dogs dataset respectively. The experimental results show that the Xception model has good generalization ability in fine-grained image classification. Because it does not need additional annotation information such as object bounding box and part annotation, the Xception model has good versatility and robustness in fine-grained image classification.