Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features are now often learned by different layers in Convolutional Neural Networks (CNNs). This paper develops a generic computer vision system based on features extracted from trained CNNs. Multiple learned features are combined into a single structure to work on different image classification tasks. The proposed system was experimentally derived by testing several approaches for extracting features from the inner layers of CNNs and using them as inputs to SVMs that are then combined by sum rule. Dimensionality reduction techniques are used to reduce the high dimensionality of inner layers. The resulting vision system is shown to significantly boost the performance of standard CNNs across a large and diverse collection of image data sets. An ensemble of different topologies using the same approach obtains state-of-the-art results on a virus data set.

翻译：计算机视野中的特征具有关键作用。最初设计这些特征的目的是通过手动算法来探测突出元素,现在这些特征往往由进化神经网络的不同层次学习。本文根据经过培训的CNN的特征开发了一个通用的计算机视觉系统。多种学习的特征被合并成一个单一的结构,用于不同的图像分类任务。拟议的系统是实验性的,通过测试从CNN内部层提取特征的几种方法并把它们用作SVM的投入,然后通过总则加以结合。多维度减少技术被用于减少内部层的高维度。由此产生的视觉系统显示,在大规模和多样化的图像数据集中大大提升标准CNN的性能。使用同一方法的不同形态的组合在病毒数据集中获得了最新的结果。