Qatris iManager: a general purpose CBIR system

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• 作者：M. Barrena ; A. Caro ; M. L. Durán ; P. G. Rodríguez…
• 关键词：Content ; based image retrieval (CBIR) ; Semantic gap ; Feature extraction ; Indexing ; Image classification ; Relevance feedback ; Automatic learning ; 68P20 ; 68P10
• 刊名：Machine Vision and Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：26
• 期：4
• 页码：423-442
• 全文大小：2,335 KB
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  27.Kri
• 作者单位：M. Barrena (1)
  A. Caro (1)
  M. L. Durán (1)
  P. G. Rodríguez (1)
  J. P. Arias-Nicolás (2)
  T. Alonso (1)
  
  1. Department of Computer Science, University of Extremadura, Avda. Universidad s/n, 10071, Cáceres, Spain
  2. Department of Mathematics, University of Extremadura, Avda. Universidad 47, 10071, Cáceres, Spain
  
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  Image Processing and Computer Vision
  Communications Engineering and Networks
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1769

文摘

Content-based image retrieval (CBIR) has drawn much interest from the research community over the past decade, as a good number of CBIR techniques, methods and systems have emerged, contributing new solutions to the issue of storing, managing and retrieving images, as database management systems do with structured data. There is undoubtedly a crucial need to characterize image content as well as subjectivity in the interpretation of this content (for which the community has coined the term “semantic gap-. In this paper the CBIR system developed by our research group, Qatris iManager, is described as a positive proposal to cope with chief issues in the field, especially the semantic gap, from a novel and original perspective. Based on color, texture and shape features, our system provides a broad range of useful operations to facilitate the storage, management, retrieval and browsing of large image collections. Local and remote image loading processes enable the population of image collections. Classification methods allow users to organize the collections according to their own interests. A multidimensional access method contributes to the efficiency in similarity searches. Parameterized similarity functions give flexibility to the search by content processes. Finally, the integrated automatic learning methods for classification and search processes teach the system about the user’s information needs. The proposed system is the result of a joint effort with different research tasks. This paper extensively describes all the system functionalities, techniques, processes and algorithms implemented.