Image Auto-annotation using a Statistical Model with Salient Regions


Tang, Jiayu, Hare, Jonathon S. and Lewis, Paul H. (2006) Image Auto-annotation using a Statistical Model with Salient Regions. At IEEE International Conference on Multimedia & Expo (ICME), Hilton Toronto, Toronto, Ontario, Canada, 09 - 12 Jul 2006.

Download

[img] PDF
Download (139Kb)

Description/Abstract

Traditionally, statistical models for image auto-annotation have been coupled with image segmentation. Considering the performance of the current segmentation algorithms, it can be meaningful to avoid a segmentation stage. In this paper, we propose a new approach to image auto-annotation using statistical models. In this approach, segmentation is avoided through the use of salient regions. The use of the statistical model results in an annotation performance which improves upon our previously proposed saliency-based word propagation technique. We also show that the use of salient regions achieves better results than the use of general image regions or segments.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Event Dates: 9-12 July
ISBNs: 1424403677
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Web & Internet Science
ePrint ID: 262826
Date Deposited: 10 Jul 2006
Last Modified: 27 Mar 2014 20:06
Further Information:Google Scholar
ISI Citation Count:0
URI: http://eprints.soton.ac.uk/id/eprint/262826

Actions (login required)

View Item View Item

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