Spatial Constrained Fine-Grained Color Name for Person Re-identification

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• 关键词：Person re ; identification ; Color name ; Bag ; of ; words ; Spatial constraint
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9516
• 期：1
• 页码：886-897
• 全文大小：1,281 KB
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• 作者单位：Yang Yang (19)
  Yuhong Yang (19) (20)
  Mang Ye (19)
  Wenxin Huang (19)
  Zheng Wang (19)
  Chao Liang (19) (20)
  Lei Yao (19)
  Chunjie Zhang (21)
  
  19. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, 430072, China
  20. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China
  21. University of Chinese Academy of Sciences, Beijing, China
  
• 丛书名：MultiMedia Modeling
• ISBN：978-3-319-27671-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Person re-identification is a key technique to match different persons observed in non-overlapping camera views. It’s a challenging problem due to the huge intra-class variations caused by illumination, poses, viewpoints, occlusions and so on. To address these issues, researchers have proposed many visual descriptors. However, these visual features may be unstable in complicated environment. Comparatively, the semantic features can be a good supplement to visual feature descriptors for its robustness. As a kind of representative semantic features, color name is utilized in this paper. The color name is a semantic description of an image and shows good robustness to photometric variations. Traditional color name based methods are limited in discriminative power due to the finite color categories, only 11 or 16 kinds. In this paper, a new fine-grained color name approach based on bag-of-words model is proposed. Moreover, spatial information, with its advantage in strengthening constraints among features in variant environment, is further applied to optimize our method. Extensive experiments conducted on benchmark datasets have shown great superiorities of the proposed method.