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An SUI-based approach to explore visual search results cluster-graphs.

Umer Rashid1, Maha Saddal1, Ghazanfar Farooq1

  • 1Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.

Plos One
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Search User Interface (SUI) for interactive image exploration. The SUI uses a cluster-graph model to improve how users find visual information, enhancing satisfaction and usability.

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Information Retrieval

Background:

  • The increasing volume of online visual content complicates information retrieval.
  • Existing image search engines often use linear layouts, hindering exploration of related image objects.
  • Users struggle to access and explore images effectively through traditional search interfaces.

Purpose of the Study:

  • To propose a new Search User Interface (SUI) for enhanced image exploration.
  • To enable non-linear reachability and interactive visualization of image search results.
  • To address the limitations of conventional image search result presentation.

Main Methods:

  • Developed a Search User Interface (SUI) approach.
  • Implemented a cluster-graph data model representing images as nodes and multimodal similarities as edges.
  • Evaluated the approach using real image datasets, usability tests, and behavioral analysis.

Main Results:

  • The proposed SUI facilitates interactive exploration and visualization of image results.
  • The cluster-graph model allows navigation through multimodal similarity relationships.
  • Usability testing indicated high user satisfaction (76.83%) and usability (83.73%).

Conclusions:

  • The novel SUI approach significantly improves the exploration of image search results.
  • Interactive visualization and non-linear navigation enhance user experience in image retrieval.
  • The findings suggest a promising direction for future image search engine design.