Proceedings chapter

Automatic image annotation with relevance feedback and latent semantic analysis

Presented at Paris (France), Jul 5-6
  • Lecture Notes in Computer Science; 4918
Publication date2007

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.

  • Database Management
  • Computer Engineering
  • Computer Applications
  • Information Storage and Retrieval
  • Multimedia Information Systems
  • Information Systems Applications (incl. Internet)
Citation (ISO format)
MORRISON, Donn Alexander, MARCHAND-MAILLET, Stéphane, BRUNO, Eric. Automatic image annotation with relevance feedback and latent semantic analysis. In: 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007. Paris (France). [s.l.] : Springer, 2007. p. 71–84. (Lecture Notes in Computer Science) doi: 10.1007/978-3-540-79860-6_6

Technical informations

Creation03/06/2015 5:12:14 PM
First validation03/06/2015 5:12:14 PM
Update time03/14/2023 10:59:10 PM
Status update03/14/2023 10:59:10 PM
Last indexation08/29/2023 3:11:17 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack