Interaction models and relevance feedback in image retrieval

  title={Interaction models and relevance feedback in image retrieval},
  author={Daniel Heesch},
Human-computer interaction is increasingly recognis ed to be an indispensable component of image retrieval systems. A typical form of interaction is that of relevance feedback whereby users supply relevance information on the retrieved images. This information can subsequently be used to optimise retrieval parameters. The first part of th e chapter provides a comprehensive review of existing relevance feedback techniques and also dis cusses a number of limitations that can be addressed more… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 42 references

Relevance Feedback in Image Retrieval Based on RSVM

2009 WASE International Conference on Information Engineering • 2009
View 6 Excerpts
Highly Influenced

Evidence combination for multi-point query learning in content-based image retrieval

IEEE Sixth International Symposium on Multimedia Software Engineering • 2004
View 7 Excerpts
Highly Influenced

The Nature of Statistical Learning Theory

Statistics for Engineering and Information Science • 2000
View 6 Excerpts
Highly Influenced

Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

Computer Vision and Image Understanding • 1999
View 4 Excerpts
Highly Influenced

Qcluster : relevance fe dback using adaptive clustering for content - based image retrieval

Chung C-W
View 4 Excerpts
Highly Influenced

Support vector machine active learning for image retrieval

ACM Multimedia • 2001
View 4 Excerpts
Highly Influenced

Image browsing: A sema

D Heesch, S Rüger

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