A Comic Retrieval System Based on Multilayer Graph Representation and Graph Mining

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• 关键词：Comics ; Attributed region adjacency graph ; Spatial relation ; Structural pattern recognition ; CBIR ; Query ; by ; example
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9069
• 期：1
• 页码：355-364
• 全文大小：1,431 KB
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• 作者单位：Thanh-Nam Le (17)
  Muhammad Muzzamil Luqman (17)
  Jean-Christophe Burie (17)
  Jean-Marc Ogier (17)
  
  17. L3i Laboratory, University of La Rochelle, 17042, La Rochelle Cedex 1, France
  
• 丛书名：Graph-Based Representations in Pattern Recognition
• ISBN：978-3-319-18224-7
• 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

Comics analysis offers a lot of interesting Content-based Image Retrieval (CBIR) applications focusing in this special type of document images. In this paper, we propose a scheme to represent and to retrieve comic-page images using attributed Region Adjacency Graphs (RAGs) and their frequent subgraphs. We first extract the graphical structures and local features of each panel in the whole comic volume, then separate different categories of local features to different layers of attributed RAGs. After that, a list of frequent subgraphs is obtained using Frequent Subgraph Mining (FSM) techniques. For CBIR purpose, the recognition and ranking are done by checking for isomorphism between the graphs representing the query image versus the list of discovered frequent subgraphs. Our experimental results show that the proposed approach can achieve reliable retrieval results for comic images browsing using query-by-example (QBE) model.