基于深度卷积特征的细粒度图像分类研究综述

细粒度图像分类问题是计算机视觉领域一项极具挑战的研究课题，其目标是对子类进行识别，如区分不同种类的鸟.由于子类别间细微的类间差异和较大的类内差异，传统的分类算法不得不依赖于大量的人工标注信息.近年来，随着深度学习的发展，深度卷积神经网络为细粒度图像分类带来了新的机遇.大量基于深度卷积特征算法的提出，促进了该领域的快速发展.本文首先从该问题的定义以及研究意义出发，介绍了细粒度图像分类算法的发展现状.之后，从强监督与弱监督两个角度对比分析了不同算法之间的差异，并比较了这些算法在常用数据集上的性能表现.最后，我们对这些算法进行了总结，并讨论了该领域未来可能的研究方向及其面临的挑战.

A Survey on Fine-grained Image Categorization UsingDeep Convolutional Features

Fine-grained image categorization is a challenging task in the field of computer vision, which aims to classify sub-categories, such as different species of birds. Due to the low inter-class but high intra-class variations, traditional categorization algorithms have to depend on a large amount of annotation information. Recently, with the advances of deep learning, deep convolutional neural networks have provided a new opportunity for fine-grained image recognition. Numerous deep convolutional feature-based algorithms have been proposed, which have advanced the development of fine-grained image research. In this paper, starting from its definition, we give a brief introduction to some recent developments in fine-grained image categorization. After that, we analyze different algorithms from the strongly supervised to and weakly supervised ones, and compare their performances on some popular datasets. Finally, we provide a brief summary of these methods as well as the potential future research direction and major challenges.