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摘要: 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.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.1) 本文责任编委 王亮
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图 2 细粒度图像数据库示意图(所有图像均取自不同类别)
Fig. 2 Illustration of fine-grained datasets (the images are sampled from different categories)
表 1 CUB200-2011[1]数据库上的算法性能比较(其中BBox指标注框信息(Bounding Box), Parts指局部区域信息)
Table 1 Performance of different algorithms in CUB200-2011[1] (where BBox refers to bounding box, Parts means part annotations)
算法 BBox
(训练)Parts
(训练)BBox
(测试)Parts
(测试)简要描述 准确率(%) CUB[1] √ √ SIFT + BoW + SVM 10.3 CUB[1] √ √ √ √ SIFT + BoW + SVM 17.3 [2mm] POOF[26] √ √ √ POOF + SVM 56.8 POOF[26] √ √ √ √ POOF + SVM 73.3 Alignment[31] √ √ Fisher + SVM 62.7 Symbiotic[30] √ √ Fisher + SVM 61 [2mm] DeCAF[25] √ √ Alex-Net + Logistic Regression 61 Part R-CNN[43] √ √ Alex-Net + Fine-Tune + SVM 73.9 Pose Normalized CNN[48] √ √ Alex-Net + Fine-Tune + SVM 75.7 Pose Normalized CNN[48] √ √ √ √ Alex-Net + Fine-Tune + SVM 85.4 [2mm] Two-level Attention[56] Alex-Net 69.7 Two-level Attention[56] VGG16-Net 77.9 Zhang et al.[12] VGG16-Net + Fine-Tune + SVM 79.3 Constellations[58] VGG19-Net + Fine-Tune + Flip + SVM 81 Bilinear CNN[13] VGG19-Net/VGG-M + Flip 84.1 Spatial Transformer Net[55] Inception[62] + Flip 84.1 
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