LG-CNN: From local parts to global discrimination for fine-grained recognition

doi:10.1016/j.patcog.2017.06.002

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	LG-CNN: From local parts to global discrimination for fine-grained recognition
	Xie, Guo-Sen1 ; Zhang, Xu-Yao1 ; Yang, Wenhan 3; Xu, Mingliang 4; Yan, Shuicheng 5; Liu, Cheng-Lin1,2
发表期刊	PATTERN RECOGNITION
	2017-11-01
卷号	71 期号:71 页码:118-131
文章类型	Article
摘要	Fine-grained recognition is one of the most difficult topics in visual recognition, which aims at distinguishing confusing categories such as bird species within a genus. The information of part and bounding boxes in fine-grained images is very important for improving the performance. However, in real applications, the part and/or bounding box annotations may not exist. This makes fine-grained recognition a challenging problem. In this paper, we propose a jointly trained Convolutional Neural Network (CNN) architecture to solve the fine-grained recognition problem without using part and bounding box information. In this framework, we first detect part candidates by calculating the gradients of feature maps of a trained CNN model w.r.t the input image and then filter out unnecessary ones by fusing two saliency detection methods. Meanwhile, two groups of global object locations are obtained based on the saliency detection methods and a segmentation method. With the filtered part candidates and approximate object locations as inputs, we construct the CNN architecture with local parts and global discrimination (LG-CNN) which consists of two CNN networks with shared weights. The upper stream of LG-CNN is focused on the part information of the input image, the bottom stream of LG-CNN is focused on the global input image. LG-CNN is jointly trained by two stream loss functions to guide the updating of the shared weights. Experiments on three popular fine-grained datasets well validate the effectiveness of our proposed LG-CNN architecture. Applying our LG-CNN architecture to generic object recognition datasets also yields superior performance over the directly fine-tuned CNN architecture with a large margin. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Fine-grained Recognition Convolutional Neural Networks Bilinear Pooling Local Parts Global Discrimination
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patcog.2017.06.002
关键词[WOS]	IMAGE CLASSIFICATION ; FISHER VECTOR ; NETWORKS ; FEATURES
收录类别	SCI
语种	英语
项目资助者	National Basic Research Program of China (973 Program)(2012CB316302) ; Strategic Priority Research Program of the CAS(XDA06040102 ; Natural Science Foundation of China(NSFC)(61472370 ; open project of State Key Laboratory of Virtual Reality Technology and System(BUAA-VR-16KF-07) ; XDB02060009) ; 61672469)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000406987400010
引用统计	被引频次：54[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20356
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Peking Univ, Beijing 100871, Peoples R China 4.Zhengzhou Univ, Ctr Interdisciplinary Informat Sci Res, Zhengzhou 450001, Henan, Peoples R China 5.Natl Univ Singapore, Singapore 119077, Singapore
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Xie, Guo-Sen,Zhang, Xu-Yao,Yang, Wenhan,et al. LG-CNN: From local parts to global discrimination for fine-grained recognition[J]. PATTERN RECOGNITION,2017,71(71):118-131.
APA	Xie, Guo-Sen,Zhang, Xu-Yao,Yang, Wenhan,Xu, Mingliang,Yan, Shuicheng,&Liu, Cheng-Lin.(2017).LG-CNN: From local parts to global discrimination for fine-grained recognition.PATTERN RECOGNITION,71(71),118-131.
MLA	Xie, Guo-Sen,et al."LG-CNN: From local parts to global discrimination for fine-grained recognition".PATTERN RECOGNITION 71.71(2017):118-131.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
LG-CNN From local pa（2717KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载