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Image Auto-annotation using 'Easy' and 'More Challenging' Training Sets

Image Auto-annotation using 'Easy' and 'More Challenging' Training Sets
Image Auto-annotation using 'Easy' and 'More Challenging' Training Sets
The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that the set is easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of 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 almost as well as the best of the more sophisticated algorithms. We then build a new image collection using the Yahoo Image Search engine (http://images.yahoo.com) and query-by-single-word searches to create a more challenging annotated set automatically. Then, using two 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. Finally we show how self-annotation can be used to improve the original annotations of our Yahoo set.
121-124
Tang, Jiayu
4f9409ac-830d-4937-867d-e06c76b8a4e1
Lewis, Paul H.
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Tang, Jiayu
4f9409ac-830d-4937-867d-e06c76b8a4e1
Lewis, Paul H.
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020

Tang, Jiayu and Lewis, Paul H. (2006) Image Auto-annotation using 'Easy' and 'More Challenging' Training Sets. 7th International Workshop on Image Analysis for Multimedia Interactive Services, Hyatt Regency, Incheon International Airport, Korea. 18 - 20 Apr 2006. pp. 121-124 .

Record type: Conference or Workshop Item (Other)

Abstract

The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that the set is easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of 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 almost as well as the best of the more sophisticated algorithms. We then build a new image collection using the Yahoo Image Search engine (http://images.yahoo.com) and query-by-single-word searches to create a more challenging annotated set automatically. Then, using two 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. Finally we show how self-annotation can be used to improve the original annotations of our Yahoo set.

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More information

Published date: 2006
Additional Information: Event Dates: April 19-21
Venue - Dates: 7th International Workshop on Image Analysis for Multimedia Interactive Services, Hyatt Regency, Incheon International Airport, Korea, 2006-04-18 - 2006-04-20
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 262477
URI: http://eprints.soton.ac.uk/id/eprint/262477
PURE UUID: 2fb396ee-9523-4ac4-b743-5503b45f2fec

Catalogue record

Date deposited: 03 May 2006
Last modified: 20 Nov 2021 07:01

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Contributors

Author: Jiayu Tang
Author: Paul H. Lewis

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