Performances of fine-grained recognition have been greatly improved thanks to the fast developments of deep convolutional neural networks (DCNN). DCNN methods often treat each image region equally. Besides, researchers often rely on visual information for classification. To solve these problems, we propose a novel discriminative semantic region selection method for fine-grained recognition (DSRS). We first select a few image regions and then use the pre-trained DCNN models to predict their semantic correlations with corresponding classes. We use both visual and semantic representations to represent image regions. The visual and semantic representations are then linearly combined for joint representation. The combination parameters are determined by considering both semantic distinctiveness and spatial-semantic correlations. We use the joint representations for classifier training. A testing image can be classified by obtaining the visual and semantic representations and encoded for joint representation and classification. Experiments on several publicly available datasets demonstrate the proposed method's superiority.

深度卷积神经网络（DCNN）的快速发展极大地提高了细粒度识别的性能。DCNN方法通常均等地对待每个图像区域。此外，研究人员经常依靠视觉信息进行分类。为了解决这些问题，我们提出了一种用于细粒度识别（DSRS）的新的区分性语义区域选择方法。我们首先选择一些图像区域，然后使用预训练的DCNN模型来预测它们与相应类的语义相关性。我们同时使用视觉和语义表示来表示图像区域。然后将视觉表示和语义表示线性组合以进行联合表示。通过同时考虑语义独特性和空间语义相关性来确定组合参数。我们使用联合表示进行分类器训练。可以通过获取视觉和语义表示来对测试图像进​​行分类，并对其进行编码以进行联合表示和分类。在几个公开可用的数据集上进行的实验证明了该方法的优越性。