Learning semantic embedding at a large scale

Min Hsuan Tsai, Jinjun Wang, Tong Zhang, Yihong Gong, Thomas S. Huang

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

Abstract

A key problem in image annotation is to learn the underlying semantics. However, finding such semantic embeddings is a challenge task and often requires large amount of tagging information. In this paper, we propose to utilize multi-modality cues by incorporating visual and textual information as embedded objects. The paper further presents a multi-task learning framework that simultaneously learns the approximation of two semantic embeddings with efficient multi-stage convex relaxation technique. The experiments show that the proposed method presents very promising performance in both memory usage and training time for large-scale dataset, as well as image classification accuracy.

Original languageEnglish (US)
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages2497-2500
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period9/11/119/14/11

Keywords

  • Semantic embedding
  • convex relaxation
  • image annotation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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