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Image re-ranking system based on closed frequent patterns

Image re-ranking system based on closed frequent patterns Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e., they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on-the-fly closed frequent patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

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References (4)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer-Verlag London
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-014-0066-8
Publisher site
See Article on Publisher Site

Abstract

Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e., they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on-the-fly closed frequent patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained.

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Sep 16, 2014

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