Topology Preserving Self-Organizing Map of Features in Image Space for Trajectory Classification

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• 关键词：Self ; organizing maps ; Image topology preservation ; Trajectory classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9108
• 期：1
• 页码：271-280
• 全文大小：307 KB
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• 作者单位：Jorge Azorin-Lopez (18)
  Marcelo Saval-Calvo (18)
  Andres Fuster-Guillo (18)
  Higinio Mora-Mora (18)
  Victor Villena-Martinez (18)
  
  18. Department of Computer Technology, University of Alicante, 03080, Alicante, Spain
  
• 丛书名：Bioinspired Computation in Artificial Systems
• ISBN：978-3-319-18833-1
• 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

Self-Organizing maps (SOM) are able to preserve topological information in the projecting space. Structure and learning algorithm of SOMs restrict the topological preservation in the map. Adjacent neurons share similar vector features. However, topological preservation from the input space is not always accomplished. In this paper, we propose a novel self-organizing feature map that is able to preserve the topological information about the scene in the image space. Extracted features in adjacent areas of an image are explicitly in adjacent areas of the self-organizing map preserving input topology (SOM-PINT). The SOM-PINT has been applied to represent and classify trajectories into high level of semantic understanding from video sequences. Experiments have been carried out using the Shopping Centre dataset of the CAVIAR database taken into account the global behaviour of an individual. Results confirm the input preservation topology in image space to obtain high performance classification for trajectory classification in contrast of traditional SOM.