Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization | |
Zhang, Luming1; Yang, Yang2; Wang, Meng1; Hong, Richang1; Nie, Liqiang3; Li, Xuelong4![]() | |
刊名 | ieee transactions on image processing
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2016-02-01 | |
卷号 | 25期号:2页码:553-565 |
关键词 | Fine-grained sub-category graphlet matching image kernel dense graph mining |
ISSN号 | 1057-7149 |
产权排序 | 4 |
英文摘要 | fine-grained image categorization is a challenging task aiming at distinguishing objects belonging to the same basic-level category, e.g., leaf or mushroom. it is a useful technique that can be applied for species recognition, face verification, and so on. most of the existing methods either have difficulties to detect discriminative object components automatically, or suffer from the limited amount of training data in each sub-category. to solve these problems, this paper proposes a new fine-grained image categorization model. the key is a dense graph mining algorithm that hierarchically localizes discriminative object parts in each image. more specifically, to mimic the human hierarchical perception mechanism, a superpixel pyramid is generated for each image. thereby, graphlets from each layer are constructed to seamlessly capture object components. intuitively, graphlets representative to each super-/sub-category is densely distributed in their feature space. thus, a dense graph mining algorithm is developed to discover graphlets representative to each super-/sub-category. finally, the discovered graphlets from pairwise images are integrated into an image kernel for fine-grained recognition. theoretically, the learned kernel can generalize several state-of-the-art image kernels. experiments on nine image sets demonstrate the advantage of our method. moreover, the discovered graphlets from each sub-category accurately capture those tiny discriminative object components, e.g., bird claws, heads, and bodies. |
WOS标题词 | science & technology ; technology |
类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
研究领域[WOS] | computer science ; engineering |
关键词[WOS] | classification ; recognition |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000383905800005 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/28382] ![]() |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
作者单位 | 1.Hefei Univ Technol, Dept Comp Sci & Informat Engn, Hefei 230009, Peoples R China 2.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610051, Peoples R China 3.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Luming,Yang, Yang,Wang, Meng,et al. Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization[J]. ieee transactions on image processing,2016,25(2):553-565. |
APA | Zhang, Luming,Yang, Yang,Wang, Meng,Hong, Richang,Nie, Liqiang,&Li, Xuelong.(2016).Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization.ieee transactions on image processing,25(2),553-565. |
MLA | Zhang, Luming,et al."Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization".ieee transactions on image processing 25.2(2016):553-565. |
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