CIDER: Concept-based image diversification, exploration, and retrieval

  • Authors:

  • Enamul Hoque;Orland Hoeber;Minglun Gong

  • Affiliations:

  • Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5 and Department of Computer Science, University of Regina, Regina, SK, Canada S4S 0A2;Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2013

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Abstract

Many of the approaches to image retrieval on the Web have their basis in text retrieval. However, when searchers are asked to describe their image needs, the resulting query is often short and potentially ambiguous. The solution we propose is to perform automatic query expansion using Wikipedia as the source knowledge base, resulting in a diversification of the search results. The outcome is a broad range of images that represent the various possible interpretations of the query. In order to assist the searcher in finding images that match their specific intentions for the query, we have developed an image organization method that uses both the conceptual information associated with each image, and the visual features extracted from the images. This, coupled with a hierarchical organization of the concepts, provides an interactive interface that takes advantage of the searchers' abilities to recognize relevant concepts, filter and focus the search results based on these concepts, and visually identify relevant images while navigating within the image space. In this paper, we outline the key features of our image retrieval system (CIDER), and present the results of a preliminary user evaluation. The results of this study illustrate the potential benefits that CIDER can provide for searchers conducting image retrieval tasks.