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Large Scale Image Indexing Using Online Non-negative Semantic Embedding

  • Jorge A. Vanegas
  • Fabio A. González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

This paper presents a novel method to address the problem of indexing a large set of images taking advantage of associated multimodal content such as text or tags. The method finds relationships between the visual and text modalities enriching the image content representation to improve the performance of content-based image search.

This method finds a mapping that connects visual and text information that allows to project new (annotated and unannotated) images to the space defined by semantic annotations, this new representation can be used to search into the collection using a query-by-example strategy and to annotate new unannotated images. The principal advantage of the proposed method is its formulation as an online learning algorithm, which can scale to deal with large image collections. The experimental evaluation shows that the proposed method, in comparison with several baseline methods, is faster and consumes less memory, keeping a competitive performance in content-based image search.

Keywords

Image Retrieval Nonnegative Matrix Factorization Mean Average Precision Semantic Space Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jorge A. Vanegas
    • 1
  • Fabio A. González
    • 1
  1. 1.MindLab Research GroupUniversidad Nacional de ColombiaBogotáColombia

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