Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis

  • Donn Morrison
  • Stéphane Marchand-Maillet
  • Eric Bruno
Conference paper

DOI: 10.1007/978-3-540-79860-6_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)
Cite this paper as:
Morrison D., Marchand-Maillet S., Bruno E. (2008) Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis. In: Boujemaa N., Detyniecki M., Nürnberger A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg

Abstract

The goal of this paper is to study the image-concept relationship as it pertains to image annotation. We demonstrate how automatic annotation of images can be implemented on partially annotated databases by learning image-concept relationships from positive examples via inter-query learning. Latent semantic analysis (LSA), a method originally designed for text retrieval, is applied to an image/session matrix where relevance feedback examples are collected from a large number of artificial queries (sessions). Singular value decomposition (SVD) is exploited during LSA to propagate image annotations using only relevance feedback information. We will show how SVD can be used to filter a noisy image/session matrix and reconstruct missing values.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Donn Morrison
    • 1
  • Stéphane Marchand-Maillet
    • 1
  • Eric Bruno
    • 1
  1. 1.Centre Universitaire InformatiqueUniversité de GenèveGenèveSwitzerland

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