IGroup: presenting web image search results in semantic clusters

ABSTRACT

Current web image search engines still rely on user typing textual description: query word(s) for visual targets. As the queries are often short, general or even ambiguous, the images in resulting pages vary in content and style. Thus, browsing with these results is likely to be tedious, frustrating and unpredictable.

IGroup, a proposed image search engine addresses these problems by presenting the result in semantic clusters. The original result set was clustered in semantic groups with a cluster name relevant to user typed queries. Instead of looking through the result pages or modifying queries, IGroup users can refine findings to the interested sub-result sets with a navigational panel, where each cluster (sub-result set) was listed with a cluster name and representative thumbnails of the cluster.

We compared IGroup with a general web image search engine: MSN, in term of efficiency, coverage, and satisfaction with a substantial user study. Our tool shows significant improvement in such criteria.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.m Miscellaneous

General Terms:
Design

Keywords:
image search interface, image search result clustering (ISRC), search result clustering (SRC), user test