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Harvesting image databases from the Web.

Florian Schroff1, Antonio Criminisi, Andrew Zisserman

  • 1Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA. gschroff@cs.ucsd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 19, 2011
PubMed
Summary
This summary is machine-generated.

This study presents an automatic method for generating high-quality images for object classes using multimodal web data. Combining text, metadata, and visual features significantly improves image ranking and retrieval.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Web image retrieval often struggles with relevance and quality.
  • Existing methods lack comprehensive feature integration for automatic ranking.

Purpose of the Study:

  • To develop an automated system for generating large sets of high-quality images for specific object classes.
  • To enhance image retrieval by combining diverse data modalities.

Main Methods:

  • Utilized a multimodal approach incorporating text, metadata, and visual features for web image gathering.
  • Employed text-based web searches for initial candidate image acquisition.
  • Implemented a two-stage reranking process: first using text/metadata, then a Support Vector Machine (SVM) visual classifier with noisy training data.
  • Investigated the impact of noisy training data on cross-validation procedures.

Main Results:

  • Successfully demonstrated a completely automatic image ranking method by integrating text/metadata and visual features.
  • Achieved improved image ranking and retrieval accuracy compared to previous approaches.
  • Validated the method across 18 diverse object classes, including animals and vehicles.

Conclusions:

  • The combined multimodal approach offers a robust and automated solution for large-scale image generation and retrieval.
  • The SVM classifier effectively refines rankings, even with imperfect training data.
  • This work advances automated content-based image retrieval systems.