On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding

  • Dmitriy Bespalov
  • Anders Lindbjerg Dahl
  • Bing Bai
  • Ali Shokoufandeh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


Concept-based representation — combined with some classifier (e.g., support vector machine) or regression analysis (e.g., linear regression) — induces a popular approach among image processing community, used to infer image labels. We propose a supervised learning procedure to obtain an embedding to a latent concept space with the pre-defined inner product. This learning procedure uses rank minimization of the sought inner product matrix, defined in the original concept space, to find an embedding to a new low dimensional space. The empirical evidence show that the proposed supervised learning method can be used in combination with another computational image embedding procedure, such as bag-of-features method, to significantly improve accuracy of label inference, while producing embedding of low complexity.


Independent Component Analysis Meat Sample Image Annotation Concept Space Latent Semantic Indexing 
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 2011

Authors and Affiliations

  • Dmitriy Bespalov
    • 1
  • Anders Lindbjerg Dahl
    • 2
  • Bing Bai
    • 3
  • Ali Shokoufandeh
    • 1
  1. 1.Department of Computer ScienceDrexel UniversityUSA
  2. 2.DTU InformaticsTechnical University of DenmarkDenmark
  3. 3.NEC Labs AmericaUSA

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