Image Retrieval Based on Query by Saliency Content, Chapter 8

Adrian Gheorghe Bors; Alex Papushoy

Image Retrieval Based on Query by Saliency Content, Chapter 8

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 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

Original language	English
Title of host publication	Visual Content Indexing and Retrieval with Psycho-Visual Models
Editors	Jenny Benois-Pineau, Patrick Le Callet
Publisher	Springer
Pages	171-209
Number of pages	38
ISBN (Print)	978-3-319-57687-9
Publication status	Published - Aug 2017

Publication series

Name	Multimedia Systems and Applications
Publisher	Springer

Cite this

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title = "Image Retrieval Based on Query by Saliency Content, Chapter 8",

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 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 usuallycorrespond 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",

author = "Bors, {Adrian Gheorghe} and Alex Papushoy",

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AB - 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 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 usuallycorrespond 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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