Retrieving Biomedical Images through Content-Based Learning from Examples Using Fine Granularity

Hao Jiang, Songhua Xu, Francis C.M. Lau

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

Abstract

Traditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.

Original languageEnglish (US)
Title of host publicationPhotonic Crystal Materials and Devices X
DOIs
StatePublished - 2012
EventPhotonic Crystal Materials and Devices X - Brussels, Belgium
Duration: Apr 16 2012Apr 19 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8425
ISSN (Print)0277-786X

Other

OtherPhotonic Crystal Materials and Devices X
CountryBelgium
CityBrussels
Period4/16/124/19/12

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Biomedical image search
  • Content-based image retrieval
  • Learning from online examples

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