Learning query-specific distance functions for large-scale web image search

Yushi Jing, Michele Covell, David Tsai, James M. Rehg

Research output: Contribution to journalArticlepeer-review

Abstract

Current Google image search adopt a hybrid search approach in which a text-based query (e.g., "Paris landmarks") is used to retrieve a set of relevant images, which are then refined by the user (e.g., by re-ranking the retrieved images based on similarity to a selected example). We conjecture that given such hybrid image search engines, learning per-query distance functions over image features can improve the estimation of image similarity. We propose scalable solutions to learning query-specific distance functions by 1) adopting a simple large-margin learning framework, 2) using the query-logs of text-based image search engine to train distance functions used in content-based systems. We evaluate the feasibility and efficacy of our proposed system through comprehensive human evaluation, and compare the results with the state-of-the-art image distance function used by Google image search.

Original languageEnglish (US)
Article number6585834
Pages (from-to)2022-2034
Number of pages13
JournalIEEE Transactions on Multimedia
Volume15
Issue number8
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Content based retrieval
  • Distance learning
  • Image processing
  • Image search
  • Search engine

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Learning query-specific distance functions for large-scale web image search'. Together they form a unique fingerprint.

Cite this