Learning a discriminative model for image annotation

Jiwei Hu, Chensheng Sun, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper introduces a new discriminative model for image annotation. To learn the discriminative model, our method divides each training image into patches, and embeds the patches into a hypergraph, so as to find the representative instances (also called exemplars) for every single class by solving the graph. Then, the feature differences between the training samples and the exemplars are used to form new feature vectors for the training process. We aim to prune the specific features for each single label and formalize the annotation task as a discriminative classification problem. The kernel methods are also employed to solve the problem. Experiments are performed using the Corel5K dataset, and provide a quite promising result when comparing with other existing methods.
Original languageEnglish
Title of host publicationAPSIPA ASC 2011 - Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011
Pages374-379
Number of pages6
Publication statusPublished - 1 Dec 2011
EventAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011 - Xi'an, China
Duration: 18 Oct 201121 Oct 2011

Conference

ConferenceAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
Country/TerritoryChina
CityXi'an
Period18/10/1121/10/11

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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