An Attention-based Approach to Content-based Image Retrieval

Bamidele, A; Stentiford, Fred; Morphett, J.

doi:10.1007/978-1-84628-429-8_16

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…The Stentiford model of visual attention [2] is also biologically inspired. It functions by suppressing areas of the image with patterns that are repeated elsewhere.…”

Section: The Stentiford Model Of Visual Attentionmentioning

confidence: 99%

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Using visual attention to extract regions of interest in the context of image retrieval

Marques

Mayron

Borba³

et al. 2006

Proceedings of the 44th Annual Southeast Regional Conference

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Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.

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“…The Stentiford model of visual attention [2] is also biologically inspired. It functions by suppressing areas of the image with patterns that are repeated elsewhere.…”

Section: The Stentiford Model Of Visual Attentionmentioning

confidence: 99%

“…Regions obtain high scores if they possess features not present elsewhere in the image. Low scores tend to be assigned to regions that have features that are common in many other parts of the image [2]".…”

Section: The Stentiford Model Of Visual Attentionmentioning

confidence: 99%

Using visual attention to extract regions of interest in the context of image retrieval

Marques

Mayron

Borba³

et al. 2006

Proceedings of the 44th Annual Southeast Regional Conference

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show abstract

“…The competition results in the selection of a few points of attention and the suppression of irrelevant material. Such a mechanism has been explored [Grigorescu et al 2003] and extended to apply to the comparison of two images in which attention is drawn to those parts that are in common rather than their absence as in the case of saliency detection in a single image [Bamidele et al 2004].…”

Section: Similarity Modelmentioning

confidence: 99%

An eye tracking interface for image search

Oyekoya

Stentiford

2006

Proceedings of the 2006 Symposium on Eye Tracking Research &Amp; Applications - ETRA '06

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This paper explores the feasibility of using an eye tracker as an image retrieval interface. A database of image similarity values between 1000 Corel images is used in the study. Results from participants performing image search tasks show that eye tracking data can be used to reach target images quicker than by random selection. The effects of the intrinsic difficulty of finding images and the time allowed for successive selections were also investigated.

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“…Such a mechanism has been explored [10] and extended to apply to the comparison of two images in which attention is drawn to those parts that are in common rather than their absence as in the case of saliency detection in a single image [3]. Whereas saliency measures require no memory of data other than the image in question, cognitive attention makes use of other stored material in order to determine similarity with an unknown image.…”

Section: Cognitive Visual Attentionmentioning

confidence: 99%

An attention based similarity measure used to identify image clusters

Bamidele¹,

Stentiford²

2005

2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005)

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This paper outlines a new attention based similarity measure and describes an application to the problem of identifying image clusters in a 4 class problem. A diverse set of images was obtained using camera phones in 4 separate locations and classification performance was tested against the true location of the images. The approach promises to have application to the unsupervised extraction of unknown numbers of clusters in larger datasets.

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An Attention-based Approach to Content-based Image Retrieval

Cited by 8 publications

References 26 publications

Using visual attention to extract regions of interest in the context of image retrieval

Using visual attention to extract regions of interest in the context of image retrieval

An eye tracking interface for image search

An attention based similarity measure used to identify image clusters

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