Complex-query web image search with concept-based relevance estimation

Abstract

Complex queries are widely used in current Web applications. They express highly specific information needs, but simply aggregating the meanings of primitive visual concepts does not perform well. To facilitate image search of complex queries, we propose a new image reranking scheme based on concept relevance estimation, which consists of Concept-Query and Concept-Image probabilistic models. Each model comprises visual, web and text relevance estimation. Our work performs weighted sum of the underlying relevance scores, a new ranking list is obtained. Considering the Web semantic context, we involve concepts by leveraging lexical and corpus-dependent knowledge, such as Wordnet and Wikipedia, with co-occurrence statistics of tags in our Flickr corpus. The experimental results showed that our scheme is significantly better than the other existing state-of-the-art approaches.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) under grant 61305062 and the Anhui Provincial Natural Science Foundation under grant 1308085QF102.

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Correspondence to Dan Guo.

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Guo, D., Gao, P. Complex-query web image search with concept-based relevance estimation. World Wide Web 19, 247–264 (2016). https://doi.org/10.1007/s11280-015-0357-x

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Keywords

  • Complex queries
  • Image reranking
  • Visual concept
  • Semantic relevance