Automatic image annotation using inverse maps from semantic embeddings

J. J. Thiagarajan, K. N. Ramamurthy, P. Sattigeri, P. T. Bremer, Andreas Spanias

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

Abstract

Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3107-3111
Number of pages5
ISBN (Print)9781479957514
DOIs
StatePublished - Jan 28 2014

Keywords

  • embedding
  • Image annotation
  • inverse map
  • RBF interpolation
  • sparse coding

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Automatic image annotation using inverse maps from semantic embeddings'. Together they form a unique fingerprint.

Cite this