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Using Multiple Segmentations for Image Auto-Annotation

Using Multiple Segmentations for Image Auto-Annotation
Using Multiple Segmentations for Image Auto-Annotation
Automatic image annotation techniques that try to identify the objects in images usually need the images to be segmented first, especially when specifically annotating image regions. The purpose of segmentation is to separate different objects in images from each other, so that objects can be processed as integral individuals. Therefore, annotation performance is highly influenced by the effectiveness of segmentation. Unfortunately, automatic segmentation is a difficult problem, and most of the current segmentation techniques do not guarantee good results. A multiple segmentations algorithm is proposed by Russell et al. to discover objects and their extent in images. In this paper, we explore the novel use of multiple segmentations in the context of image auto-annotation. It is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach. Three different levels of segmentations were generated for a 5000 image collection. Experimental results show that image auto-annotation achieves better performance when using all three segmentation levels together than using any single one on its own.
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) Using Multiple Segmentations for Image Auto-Annotation. ACM International Conference on Image and Video Retrieval (CIVR), Amsterdam, The, Netherlands. 08 - 10 Jul 2007.

Record type: Conference or Workshop Item (Other)

Abstract

Automatic image annotation techniques that try to identify the objects in images usually need the images to be segmented first, especially when specifically annotating image regions. The purpose of segmentation is to separate different objects in images from each other, so that objects can be processed as integral individuals. Therefore, annotation performance is highly influenced by the effectiveness of segmentation. Unfortunately, automatic segmentation is a difficult problem, and most of the current segmentation techniques do not guarantee good results. A multiple segmentations algorithm is proposed by Russell et al. to discover objects and their extent in images. In this paper, we explore the novel use of multiple segmentations in the context of image auto-annotation. It is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach. Three different levels of segmentations were generated for a 5000 image collection. Experimental results show that image auto-annotation achieves better performance when using all three segmentation levels together than using any single one on its own.

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More information

Published date: 2007
Additional Information: Event Dates: July 9-11
Venue - Dates: ACM International Conference on Image and Video Retrieval (CIVR), Amsterdam, The, Netherlands, 2007-07-08 - 2007-07-10
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 264327
URI: http://eprints.soton.ac.uk/id/eprint/264327
PURE UUID: 15d1f5b2-dbac-4414-bfe1-61e47ab6b8e8

Catalogue record

Date deposited: 19 Jul 2007
Last modified: 20 Nov 2021 07:44

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Contributors

Author: Jiayu Tang
Author: Paul Lewis

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