The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

An Image Based Feature Space and Mapping for Linking Regions and Words

An Image Based Feature Space and Mapping for Linking Regions and Words
An Image Based Feature Space and Mapping for Linking Regions and Words
We propose an image based feature space and define a mapping of both image regions and textual labels into that space. We believe the embedding of both image regions and labels into the same space in this way is novel, and makes object recognition more straightforward. Each dimension of the space corresponds to an image from the database. The coordinates of an image segment(region) are calculated based on its distance to the closest segment within each of the images, while the coordinates of a label are generated based on their association with the images. As a result, similar image segments associated with the same objects are clustered together in this feature space, and should also be close to the labels representing the object. The link between image regions and words can be discovered from their separation in the feature space. The algorithm is applied to an image collection and preliminary results are encouraging.
Object Recognition, Image Auto-Annotation
29-35
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. (2007) An Image Based Feature Space and Mapping for Linking Regions and Words. 2nd International Conference on Computer Vision Theory and Applications (VISAPP), Barcelona, Spain. 08 - 11 Mar 2007. pp. 29-35 .

Record type: Conference or Workshop Item (Other)

Abstract

We propose an image based feature space and define a mapping of both image regions and textual labels into that space. We believe the embedding of both image regions and labels into the same space in this way is novel, and makes object recognition more straightforward. Each dimension of the space corresponds to an image from the database. The coordinates of an image segment(region) are calculated based on its distance to the closest segment within each of the images, while the coordinates of a label are generated based on their association with the images. As a result, similar image segments associated with the same objects are clustered together in this feature space, and should also be close to the labels representing the object. The link between image regions and words can be discovered from their separation in the feature space. The algorithm is applied to an image collection and preliminary results are encouraging.

Text
129_Tang.pdf - Other
Download (1MB)

More information

Published date: 2007
Additional Information: Event Dates: March 8-11
Venue - Dates: 2nd International Conference on Computer Vision Theory and Applications (VISAPP), Barcelona, Spain, 2007-03-08 - 2007-03-11
Keywords: Object Recognition, Image Auto-Annotation
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 263691
URI: http://eprints.soton.ac.uk/id/eprint/263691
PURE UUID: 81d13350-004b-4bc7-86c3-c561b83f83ce

Catalogue record

Date deposited: 13 Mar 2007
Last modified: 20 Nov 2021 07:31

Export record

Contributors

Author: Jiayu Tang
Author: Paul H. Lewis

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×