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Article
Publication date: 21 March 2016

Tao Liu, Zhixiang Fang, Qingzhou Mao, Qingquan Li and Xing Zhang

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims…

Abstract

Purpose

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes.

Design/methodology/approach

In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency.

Findings

The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes.

Originality/value

This paper presents a method that improves the image-based visual saliency model.

Details

Sensor Review, vol. 36 no. 2
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 9 April 2021

Qiang Yang, Yuanjian Zhou, Yushi Jiang and Jiale Huo

This study aims to explore whether creativity can overcome banner blindness in the viewing of web pages and demonstrate how visual saliency and banner-page congruity…

Abstract

Purpose

This study aims to explore whether creativity can overcome banner blindness in the viewing of web pages and demonstrate how visual saliency and banner-page congruity constitute the boundary conditions for creativity to improve memory for banner ads.

Design/methodology/approach

Three studies were conducted to understand the influence of advertising creativity and banner blindness on recognition of banner ads, which were assessed using questionnaires and bias adjustment. The roles of online user tasks (goal-directed vs free-viewing), visual saliency (high vs low) and banner-page congruity (congruent vs incongruent) were considered.

Findings

The findings suggest that creativity alone is not sufficient to overcome the banner blindness phenomenon. Specifically, in goal-directed tasks, the effect of creativity on recognition of banner ads is dependent on banner ads’ visual saliency and banner-page congruity. Creative banners are high on visual saliency, and banner-page congruity yields higher recognition rates.

Practical implications

Creativity matters for attracting consumer attention. And in a web page context, where banner blindness prevails, the design of banners becomes even more important in this respect. Given the prominence of banners in online marketing, it is also necessary to tap the potential of creativity of banner ads.

Originality/value

First, focusing on how creativity influences memory for banner ads across distinct online user tasks not just provides promising theoretical insight on the tackling of banner blindness but also enriches research on advertising creativity. Second, contrary to the popular belief of extant literature, the findings suggest that, in a web page context, improvement in memory for banner ads via creativity is subject to certain boundary conditions. Third, a computational neuroscience software program was used in this study to assess the visual saliency of banner ads, whereas signal detection theory was used for adjustment of recognition scores. This interdisciplinary examination combining the two perspectives sheds new light on online advertising research.

Details

Journal of Research in Interactive Marketing, vol. 15 no. 2
Type: Research Article
ISSN: 2040-7122

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Article
Publication date: 1 August 2016

Chunlei Li, Ruimin Yang, Zhoufeng Liu, Guangshuai Gao and Qiuli Liu

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned…

Abstract

Purpose

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

Design/methodology/approach

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Findings

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

Originality/value

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

Details

International Journal of Clothing Science and Technology, vol. 28 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 14 August 2017

Ning Xian

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic…

Abstract

Purpose

The purpose of this paper is to propose a new algorithm chaotic pigeon-inspired optimization (CPIO), which can effectively improve the computing efficiency of the basic Itti’s model for saliency-based detection. The CPIO algorithm and relevant applications are aimed at air surveillance for target detection.

Design/methodology/approach

To compare the improvements of the performance on Itti’s model, three bio-inspired algorithms including particle swarm optimization (PSO), brain storm optimization (BSO) and CPIO are applied to optimize the weight coefficients of each feature map in the saliency computation.

Findings

According to the experimental results in optimized Itti’s model, CPIO outperforms PSO in terms of computing efficiency and is superior to BSO in terms of searching ability. Therefore, CPIO provides the best overall properties among the three algorithms.

Practical implications

The algorithm proposed in this paper can be extensively applied for fast, accurate and multi-target detections in aerial images.

Originality/value

CPIO algorithm is originally proposed, which is very promising in solving complicated optimization problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 15 April 2020

Xiaoliang Qian, Jing Li, Jianwei Zhang, Wenhao Zhang, Weichao Yue, Qing-E Wu, Huanlong Zhang, Yuanyuan Wu and Wei Wang

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract…

Abstract

Purpose

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

Design/methodology/approach

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Findings

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

Originality/value

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

Details

Sensor Review, vol. 40 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 14 October 2013

Dong Liu, Ming Cong, Yu Du and Clarence W. de Silva

Indoor robotic tasks frequently specify objects. For these applications, this paper aims to propose an object-based attention method using task-relevant feature for target…

Abstract

Purpose

Indoor robotic tasks frequently specify objects. For these applications, this paper aims to propose an object-based attention method using task-relevant feature for target selection. The task-relevant feature(s) are deduced from the learned object representation in semantic memory (SM), and low dimensional bias feature templates are obtained using Gaussian mixture model (GMM) to get an efficient attention process. This method can be used to select target in a scene which forms a task-specific representation of the environment and improves the scene understanding by driving the robot to a position in which the objects of interest can be detected with a smaller error probability.

Design/methodology/approach

Task definition and object representation in SM are proposed, and bias feature templates are obtained using GMM deduction for features from high dimension to low dimension. Mean shift method is used to segment the visual scene into discrete proto-objects. Given a task-specific object, the top-down bias attention uses obtained statistical knowledge of the visual features of the desired target to impact proto-objects and generate the saliency map by combining with the bottom-up saliency-based attention so as to maximize target detection speed.

Findings

Experimental results show that the proposed GMM-based attention model provides an effective and efficient method for task-specific target selection under different conditions. The promising results show that the method may provide good approximation to how humans combine target cues to optimize target selection.

Practical implications

The present method has been successfully applied in plenty of natural scenes of indoor robotic tasks. The proposed method has a wide range of applications and is using for an intelligent homecare robot cognitive control project. Due to the computational cost, the current implementation of this method has some limitations in real-time application.

Originality/value

The novel attention model which uses GMM to get the bias feature templates is proposed for attention competition. It provides a solution for object-based attention, and it is effective and efficient to improve search speed due to the autonomous deduction of features. The proposed model is adaptive without requiring predefined distinct types of features for task-specific objects.

Details

Industrial Robot: An International Journal, vol. 40 no. 6
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 26 August 2014

Xing Wang, Zhenfeng Shao, Xiran Zhou and Jun Liu

This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and…

Abstract

Purpose

This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and information acquisition technologies, more complex objects appear in high-resolution remote sensing images. Traditional visual features are no longer precise enough to describe the images.

Design/methodology/approach

A novel remote sensing image retrieval method based on VSP (visual salient point) features is proposed in this paper. A key point detector and descriptor are used to extract the critical features and their descriptors in remote sensing images. A visual attention model is adopted to calculate the saliency map of the images, separating the salient regions from the background in the images. The key points in the salient regions are then extracted and defined as VSPs. The VSP features can then be constructed. The similarity between images is measured using the VSP features.

Findings

According to the experiment results, compared with traditional visual features, VSP features are more precise and stable in representing diverse remote sensing images. The proposed method performs better than the traditional methods in image retrieval precision.

Originality/value

This paper presents a novel remote sensing image retrieval method based on VSP features.

Details

Sensor Review, vol. 34 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 2 February 2018

Felix Otto and Christopher Rumpf

Visual animation of sponsorship signage has become a frequently used technique at televised sports with the aim to increase viewer attention. The purpose of this paper is…

Abstract

Purpose

Visual animation of sponsorship signage has become a frequently used technique at televised sports with the aim to increase viewer attention. The purpose of this paper is to investigate the impact of animation intensity of sponsorship signage on sport viewers’ attention and to examine viewers’ visual confusion as a reaction to increasing animation intensity.

Design/methodology/approach

Based on a lab experiment, eye-tracking methodology was applied to analyze the participants’ visual attention to animated sponsorship signage. The stimulus films showed a highlight video clip of a tennis match and included five different intensity levels of animated signage. The hypothesized causal relationships were tested by using linear regression analysis and structural equation modeling.

Findings

The results demonstrate that animation intensity of sponsorship signage positively influences sport viewers’ attention. The findings also reveal that animation intensity has no significant effect on sport viewers’ visual confusion.

Practical implications

The findings suggest the use of higher animation intensity levels for effective sponsorship communication in sports broadcasts. Furthermore, there is still more potential to improve sponsorship communication at televised tennis events as viewer confusion was not affected by animation intensity.

Originality/value

This research contributes to the body of knowledge by taking into account different intensity levels of animated sponsorship signage in a tennis event context. It is the first study that demonstrates the impact of animation intensity to improve sponsorship communication at televised sporting events.

Details

Sport, Business and Management: An International Journal, vol. 8 no. 2
Type: Research Article
ISSN: 2042-678X

Keywords

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Article
Publication date: 8 January 2020

Wagner Ladeira, Fernando de Oliveira Santini and William Carvalho Jardim

This study was predicated on gaze behaviour in front-of-shelf orientation. The purpose of this paper is to analyse the effect of the presence (absence) of competing brands…

Abstract

Purpose

This study was predicated on gaze behaviour in front-of-shelf orientation. The purpose of this paper is to analyse the effect of the presence (absence) of competing brands on consumer attention in front-of-shelf orientation. The effects on visual attention investigated on the shelf were eye scan path of the total available area, information acquisition in extremities and mental effort.

Design/methodology/approach

Two experiments were performed using eye-tracking technology. The first study was conducted in a closed and static environment. The second study was performed in an open and dynamic environment. In these studies, the authors used, as an independent variable, the arrangement of brands on shelves (presence vs absence of competing) and evaluated the variations in the visual attention through three dependent variables: eye scan path of the total available area, information acquisition in extremities and mental effort.

Findings

Three hypotheses were tested. The first hypothesis confirmed that scenarios of competitive brands are rather composed of natural complex scenes, so there is a greater number of eye fixations needed to identify and locate objects. In addition, the second hypothesis demonstrated that, in scenarios of competitive brands, there is an acceleration of information acquisitions causing an increase in peripheral vision at the ends of the shelf. Finally, the third hypothesis demonstrated that the presence of a greater attention effort in the scenario of competing brands was verified, since the mental effort variables (revisiting the shelf, noting and re-examining) were greater than in the scenario of non-competing brands.

Research limitations/implications

Limitations of this study may be associated with the absence of top-down factors and a lack of results associated with evaluation and verification phases.

Originality/value

Gaze behaviour is susceptible to the information derived from the absence and presence of competing brands.

Details

International Journal of Retail & Distribution Management, vol. 48 no. 2
Type: Research Article
ISSN: 0959-0552

Keywords

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Article
Publication date: 7 September 2015

Zhoufeng Liu, Chunlei Li, Quanjun Zhao, Liang Liao and Yan Dong

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based…

Abstract

Purpose

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

Design/methodology/approach

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

Findings

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

Originality/value

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

Details

International Journal of Clothing Science and Technology, vol. 27 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

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