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Proceedings Paper

Efficient re-indexing of automatically annotated image collections using keyword combination
Author(s): Alexei Yavlinsky; Stefan Rüger
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Paper Abstract

This paper presents a framework for improving the image index obtained by automated image annotation. Within this framework, the technique of keyword combination is used for fast image re-indexing based on initial automated annotations. It aims to tackle the challenges of limited vocabulary size and low annotation accuracies resulting from differences between training and test collections. It is useful for situations when these two problems are not anticipated at the time of annotation. We show that based on example images from the automatically annotated collection, it is often possible to find multiple keyword queries that can retrieve new image concepts which are not present in the training vocabulary, and improve retrieval results of those that are already present. We demonstrate that this can be done at a very small computational cost and at an acceptable performance tradeoff, compared to traditional annotation models. We present a simple, robust, and computationally efficient approach for finding an appropriate set of keywords for a given target concept. We report results on TRECVID 2005, Getty Image Archive, and Web image datasets, the last two of which were specifically constructed to support realistic retrieval scenarios.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6506, Multimedia Content Access: Algorithms and Systems, 650608 (29 January 2007); doi: 10.1117/12.702913
Show Author Affiliations
Alexei Yavlinsky, Imperial College London (United Kingdom)
Stefan Rüger, Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 6506:
Multimedia Content Access: Algorithms and Systems
Alan Hanjalic; Raimondo Schettini; Nicu Sebe, Editor(s)

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