自动化学报2017，Vol.43Issue(8)：1306-1318,13.DOI:10.16383/j.aas.2017.c160425
基于深度卷积特征的细粒度图像分类研究综述
A Survey on Fine-grained Image Categorization Using Deep Convolutional Features
摘要
Abstract
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.关键词
细粒度图像分类/深度学习/卷积神经网络/计算机视觉Key words
Fine-grained image categorization/deep learning/convolutional neural networks/computer vision引用本文复制引用
罗建豪,吴建鑫..基于深度卷积特征的细粒度图像分类研究综述[J].自动化学报,2017,43(8):1306-1318,13.基金项目
国家自然科学基金(61422203) 资助Supported by National Natural Science Foundation of China (61422203) （61422203）
