Visual query suggestion: Towards capturing user intent in internet image search

  • Authors:

  • Zheng-Jun Zha;Linjun Yang;Tao Mei;Meng Wang;Zengfu Wang;Tat-Seng Chua;Xian-Sheng Hua

  • Affiliations:

  • University of Science and Technology of China, Hefei, Anhui, China and National University of Singapore, Singapore;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;University of Science and Technology of China, Hefei, Anhui, China;National University of Singapore, Singapore;Microsoft Research Asia, Beijing, China

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2010

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Abstract

Query suggestion is an effective approach to bridge the Intention Gap between the users' search intents and queries. Most existing search engines are able to automatically suggest a list of textual query terms based on users' current query input, which can be called Textual Query Suggestion. This article proposes a new query suggestion scheme named Visual Query Suggestion (VQS) which is dedicated to image search. VQS provides a more effective query interface to help users to precisely express their search intents by joint text and image suggestions. When a user submits a textual query, VQS first provides a list of suggestions, each containing a keyword and a collection of representative images in a dropdown menu. Once the user selects one of the suggestions, the corresponding keyword will be added to complement the initial query as the new textual query, while the image collection will be used as the visual query to further represent the search intent. VQS then performs image search based on the new textual query using text search techniques, as well as content-based visual retrieval to refine the search results by using the corresponding images as query examples. We compare VQS against three popular image search engines, and show that VQS outperforms these engines in terms of both the quality of query suggestion and the search performance.