论文标题
多层深度提取视觉纹理识别
Multilayer deep feature extraction for visual texture recognition
论文作者
论文摘要
卷积神经网络已在图像分类方面取得了成功的结果，从而实现了超过人类水平的实时结果。但是，纹理图像仍然对这些模型构成一些挑战，例如，在出现这些图像，高层间相似性的几个问题中的数据可用性有限，没有代表对象的全局观点等。在这种情况下，本文的重点是提高纹理分类中卷积神经网络的准确性。这是通过从验证的神经网络的多个卷积层中提取特征并使用Fisher载体聚集了此类特征来完成的。使用早期卷积层的特征的原因是获得了较少域的信息。我们验证方法对基准数据集的纹理分类以及巴西植物物种识别的实际任务的有效性。在这两种情况下，在多层上计算出的Fisher矢量都优于最先进的方法，证实了早期的卷积层提供了有关分类纹理图像的重要信息。
Convolutional neural networks have shown successful results in image classification achieving real-time results superior to the human level. However, texture images still pose some challenge to these models due, for example, to the limited availability of data for training in several problems where these images appear, high inter-class similarity, the absence of a global viewpoint of the object represented, and others. In this context, the present paper is focused on improving the accuracy of convolutional neural networks in texture classification. This is done by extracting features from multiple convolutional layers of a pretrained neural network and aggregating such features using Fisher vector. The reason for using features from earlier convolutional layers is obtaining information that is less domain specific. We verify the effectiveness of our method on texture classification of benchmark datasets, as well as on a practical task of Brazilian plant species identification. In both scenarios, Fisher vectors calculated on multiple layers outperform state-of-art methods, confirming that early convolutional layers provide important information about the texture image for classification.
