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Image Retrieval with Textual Label Similarity Features

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  • Alicia Sagae
  • Scott E. Fahlman

Abstract

This article presents a knowledge‐based solution for retrieving English descriptions of images. We analyse the errors made by a baseline system that relies on term frequency, and we find that the task requires deeper semantic representation. Our solution is to perform incremental, task‐driven development of an ontology. Ontological features are then applied in a machine‐learning algorithm for ranking candidate image descriptions. This work demonstrates the advantage of combining knowledge‐based and statistical approaches for text retrieval, and it establishes the important result that an empirically tuned task‐specific ontology performs better than a domain‐general resource like WordNet, even on previously unseen examples. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Alicia Sagae & Scott E. Fahlman, 2015. "Image Retrieval with Textual Label Similarity Features," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(1), pages 101-113, January.
    • Handle: RePEc:wly:isacfm:v:22:y:2015:i:1:p:101-113
    DOI: 10.1002/isaf.1364
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