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A Study of Quality Issues for Image Auto-Annotation with the Corel Data-Set

A Study of Quality Issues for Image Auto-Annotation with the Corel Data-Set
A Study of Quality Issues for Image Auto-Annotation with the Corel Data-Set
The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that Corel images are relatively easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of data-sets like the Corel set for effective auto-annotation evaluation. We first compare the performance of several annotation algorithms using the Corel set and find that simple near neighbour propagation techniques perform fairly well. A Support Vector Machine (SVM) based annotation method achieves even better results, almost as good as the best found in the literature. We then build a new image collection using the Yahoo Image Search engine and query-by-single-word searches to create a more challenging annotated set automatically. Then, using three very different image annotation methods, we demonstrate some of the problems of annotation using the Corel set compared with the Yahoo based training set. In both cases the training sets are used to create a set of annotations for the Corel test set.
Corel Image set, Image Auto-Annotation, Support Vector Machine (SVM)
384-389
Tang, Jiayu
4f9409ac-830d-4937-867d-e06c76b8a4e1
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Tang, Jiayu
4f9409ac-830d-4937-867d-e06c76b8a4e1
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020

Tang, Jiayu and Lewis, Paul (2007) A Study of Quality Issues for Image Auto-Annotation with the Corel Data-Set. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 1 (NO. 3), 384-389.

Record type: Article

Abstract

The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that Corel images are relatively easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of data-sets like the Corel set for effective auto-annotation evaluation. We first compare the performance of several annotation algorithms using the Corel set and find that simple near neighbour propagation techniques perform fairly well. A Support Vector Machine (SVM) based annotation method achieves even better results, almost as good as the best found in the literature. We then build a new image collection using the Yahoo Image Search engine and query-by-single-word searches to create a more challenging annotated set automatically. Then, using three very different image annotation methods, we demonstrate some of the problems of annotation using the Corel set compared with the Yahoo based training set. In both cases the training sets are used to create a set of annotations for the Corel test set.

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More information

Published date: March 2007
Keywords: Corel Image set, Image Auto-Annotation, Support Vector Machine (SVM)
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 263437
URI: https://eprints.soton.ac.uk/id/eprint/263437
PURE UUID: de93d994-7994-4866-a3ab-c5690f454008

Catalogue record

Date deposited: 15 Feb 2007
Last modified: 18 Jul 2017 07:45

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