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Multimodal Image Retrieval Based on Keywords and Low-Level Image Features

  • Miran Pobar
  • Marina Ivašić-Kos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9398)

Abstract

Image retrieval approaches dealing with the complex problem of image search and retrieval in very large image datasets proposed so far can be roughly divided into those that use text descriptions of images (text-based image retrieval) and those that compare visual image content (content-based image retrieval). Both approaches have their strengths and drawbacks especially in the case of searching for images in general unconstrained domain. To take advantage of both approaches, we propose a multimodal framework that uses both keywords and visual properties of images. Keywords are used to determine the semantics of the query while the example image presents the visual impression (perceptual and structural information) that retrieved images should suit. In the paper, the overview of the proposed multimodal image retrieval framework is presented. For computing the content-based similarity between images different feature sets and metrics were tested. The procedure is described with Corel and Flickr images from the domain of outdoor scenes.

Keywords

Image retrieval Multimodal query Content-based similarity 

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  1. 1.Department of InformaticsUniversity of RijekaRijekaCroatia

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