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Visual Content Indexing and Retrieval with Psycho-Visual Models Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Image Retrieval Based on Query by Saliency Content

Authors : Adrian G. Bors, Alex Papushoy

Published in: Visual Content Indexing and Retrieval with Psycho-Visual Models

Publisher: Springer International Publishing

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Abstract

This chapter outlines a content based image retrieval (CBIR) methodology that takes into account the saliency in images. Natural images are depictions of real-life objects and scenes, usually set in cluttered environments. The performance of image retrieval in these scenarios may suffer because there is no way of knowing which parts of the image are of interest to the user. The human visual system provides a clue to what would be of interest in the image, by involuntarily shifting the focus of attention to salient image areas. The application of computational models of selective visual attention to image understanding can produce better, unsupervised retrieval results by identifying perceptually important areas of the image that usually correspond to its semantic meaning, whilst discarding irrelevant information. This chapter explores the construction of a retrieval system incorporating a visual attention model and proposes a new method for selecting salient image regions, as well as embedding an improved representation for salient image edges for determining global image saliency.

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previous chapter Information: Theoretical Model for Saliency Prediction—Application to Attentive CBIR
next chapter Visual Saliency for the Visualization of Digital Paintings
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Metadata
Title
Image Retrieval Based on Query by Saliency Content
Authors
Adrian G. Bors
Alex Papushoy
Copyright Year
2017
Book
Visual Content Indexing and Retrieval with Psycho-Visual Models

Print ISBN: 978-3-319-57686-2

Electronic ISBN: 978-3-319-57687-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57687-9_8

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