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

Marques, Oge; Mayron, Liam M.; Borba, Gustavo Benvenutti; Gamba, Humberto Remigio

doi:10.1145/1185448.1185588

Cited by 36 publications

(21 citation statements)

References 28 publications

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“…A method of image retrieval based on the information provided by histogram analysis of the intensity or grayscale values of images is proposed in [34]. In [35], Marques et al segmented out regions that match salient Region of Interest (ROI) defined by studying human perception. Also, Wang et al applied the same ROI idea in their RBIR work [36].…”

Section: Region-based Image Retrievalmentioning

confidence: 99%

Narrowing Semantic Gap in Content-based Image Retrieval

Yang

Zhu

2012

2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

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Due to the low-level image features it utilizes, the semantic gap problem is hard to bridge and performance of CBIR systems is still far away from users' expectation. Image annotation, region-based image retrieval and relevance feedback are three main approaches for narrowing the "semantic gap". In this paper, recent development in these fields are reviewed and some future directions are proposed in the end.

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Section: Region-based Image Retrievalmentioning

confidence: 99%

Narrowing Semantic Gap in Content-based Image Retrieval

Yang

Zhu

2012

2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

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“…There is Marques et al who segmented the images out to regions that match salient Regions of Interest (ROI) defined by studying human perception [9]. And Wang et al who constructed their ROI on the basis of the wavelet decomposition of an image and then, they used the lowest frequency sub-band as an approximation of the original image [10].…”

Section: Related Workmentioning

confidence: 99%

A Proposed Framework for a Distributed CBIR System based on Salient Regions and RF Techniques

El-Desoky¹,

Arafat²,

Sakr³

2012

IJCA

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Digital images databases open the way for content-based searching. Content Based Image Retrieval occupies a well ranked position among the research areas as it provides the practical solution for narrowing the semantic gap between the image retrieval process and the human perception. The main objective of this paper is to propose a framework for region content based image retrieval based on a distributed clustered image dataset. The proposed framework introduces a new perspective to measure the similarity between the image query and the clustered dataset images. Moreover, a development by adopting three relevance feedback techniques is used to refine the results of the retrieval system which are the well known Query Point Movement and Query Expansion, besides to the proposed third technique which is Query Modified ReWeighting technique.

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“…In [30], Marques et al segmented out regions that match salient Region of Interest (ROI) defined by studying human perception. They considered the problem of semantic region segmentation as a ROI extraction problem.…”

Section: Region Segmentation Problemmentioning

confidence: 99%

A Review of Region-Based Image Retrieval

Huang

Gao

Chan

2008

J Sign Process Syst Sign Image Video Technol

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Searching interested images based on visual properties or contents of images is a challenging problem and it has received much attention from researchers in the last 20 years. The gap between lowlevel visual features and high-level semantic understanding of images, which is also known as the semantic gap problem, is the bottleneck to further improvement of the performance of a content-based image retrieval system. In order to solve this semantic gap problem, one of the most popular approaches in recent years is to change the focus from the global content description of images into the local content description by regions (region-based image retrieval) or even the objects in images (object-based image retrieval). Although much research in region-based image retrieval has already been done, there are still three main problems need to be tackled properly: (a) local region-based features, (b) similarity measures, and (c) relevance feedback based on regions. In this paper, we review some recent development in region-based image retrieval with respect to the above three problems and propose some future directions for region-based image retrieval research towards the end of this paper.

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

Cited by 36 publications

References 28 publications

Narrowing Semantic Gap in Content-based Image Retrieval

Narrowing Semantic Gap in Content-based Image Retrieval

A Proposed Framework for a Distributed CBIR System based on Salient Regions and RF Techniques

A Review of Region-Based Image Retrieval

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