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Category co-occurrence modeling for large scale scene recognition
Song, Xinhang1; Jiang, Shuqiang1; Herranz, Luis1; Kong, Yan2; Zheng, Kai3
2016-11-01
发表期刊PATTERN RECOGNITION
ISSN0031-3203
卷号59页码:98-111
摘要Scene recognition involves complex reasoning from low-level local features to high-level scene categories. The large semantic gap motivates that most methods model scenes resorting to mid-level representations (e.g. objects, topics). However, this implies an additional mid-level vocabulary and has implications in training and inference. In contrast, the semantic multinomial (SMN) represents patches directly in the scene-level semantic space, which leads to ambiguity when aggregated to a global image representation. Fortunately, this ambiguity appears in the form of scene category co-occurrences which can be modeled a posteriori with a classifier. In this paper we observe that these patterns are essentially local rather than global, sparse, and consistent across SMNs obtained from multiple visual features. We propose a co-occurrence modeling framework where we exploit all these patterns jointly in a common semantic space, combining both supervised and unsupervised learning. Based on this framework we can integrate multiple features and design embeddings for large scale recognition directly in the scene-level space. Finally, we use the co-occurrence modeling framework to develop new scene representations, which experiments show that outperform previous SMN-based representations. (C) 2016 Elsevier Ltd. All rights reserved.
关键词Scene recognition Co-occurrence modeling Semantic space Feature embedding Multiple feature combination Large scale image recognition
DOI10.1016/j.patcog.2016.01.019
收录类别SCI
语种英语
资助项目National Basic Research 973 Program of China[2012CB316400] ; National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[61322212] ; National Natural Science Foundation of China[61550110505] ; National High Technology Research and Development 863 Program of China[2014AA015202] ; Lenovo Outstanding Young Scientists Program (LOYS) ; CAS President's International Fellowship Initiative[2011Y1GB05]
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000383007800010
出版者ELSEVIER SCI LTD
引用统计
被引频次:17[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/8157
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jiang, Shuqiang
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Soochow Univ, Sch Comp Sci, Suzhou, Jiangsu, Peoples R China
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GB/T 7714
Song, Xinhang,Jiang, Shuqiang,Herranz, Luis,et al. Category co-occurrence modeling for large scale scene recognition[J]. PATTERN RECOGNITION,2016,59:98-111.
APA Song, Xinhang,Jiang, Shuqiang,Herranz, Luis,Kong, Yan,&Zheng, Kai.(2016).Category co-occurrence modeling for large scale scene recognition.PATTERN RECOGNITION,59,98-111.
MLA Song, Xinhang,et al."Category co-occurrence modeling for large scale scene recognition".PATTERN RECOGNITION 59(2016):98-111.
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