Intuitive Large Image Database Browsing Using Perceptual Similarity Enriched by Crowds

  • Stefano Padilla
  • Fraser Halley
  • David A. Robb
  • Mike J. Chantler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)

Abstract

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Keywords

Databases Images Navigation Browsers Perception Crowd Sourcing Similarity Retrieval Indexing Clustering Abstracts Textures 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefano Padilla
    • 1
  • Fraser Halley
    • 1
  • David A. Robb
    • 1
  • Mike J. Chantler
    • 1
  1. 1.The Texture Lab., School of Mathematical & Computer SciencesHeriot-Watt UniversityEdinburghUK

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