Cross-modal retrieval: A pairwise classification approach

Aditya Krishna Menon, Didi Sudan, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Content is increasingly available in multiple modalities (such as images, text, and video), each of which provides a different representation of some entity. The cross-modal retrieval problem is: given the representation of an entity in one modality, find its best representation in all other modalities. We propose a novel approach to this problem based on pairwise classification. The approach seamlessly applies to both the settings where ground-truth annotations for the entities are absent and present. In the former case, the approach considers both positive and unlabelled links that arise in standard cross-modal retrieval datasets. Empirical comparisons show improvements over state-of-the-art methods for cross-modal retrieval.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
PublisherSociety for Industrial and Applied Mathematics Publications
Pages199-207
Number of pages9
ISBN (Print)9781510811522
Publication statusPublished - 2015
Externally publishedYes
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: 30 Apr 20152 May 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period30/4/152/5/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Menon, A. K., Sudan, D., & Chawla, S. (2015). Cross-modal retrieval: A pairwise classification approach. In SIAM International Conference on Data Mining 2015, SDM 2015 (pp. 199-207). Society for Industrial and Applied Mathematics Publications.