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

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• 作者：Dan Guo ; Pengfei Gao
• 关键词：Complex queries ; Image reranking ; Visual concept ; Semantic relevance
• 刊名：World Wide Web
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：19
• 期：2
• 页码：247-264
• 全文大小：1,621 KB
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• 作者单位：Dan Guo (1)
  Pengfei Gao (1)
  
  1. School of Computer and Information, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui, 230009, China
  
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications and The Internet
  Database Management
  Operating Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-1413

文摘

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. Keywords Complex queries Image reranking Visual concept Semantic relevance