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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV recognize targets, especially in complex noisy environment.

The hybrid model of saliency-based visual attention and artificial bee colony (ABC) algorithm is established for edge detection of UCAV. Visual attention can extract the region of interesting objects, and this approach can narrow the searching region for object segmentation, which can reduce the computational complexity. An improved ABC algorithm is applied in edge detection of the salient region.

This work improved ABC algorithm by modifying the search strategy and adding some limits, so that it can be applied to edge detection problem. A hybrid model of saliency-based visual attention and ABC algorithm is developed. Experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee efficient target localization, with accurate edge detection in complex noisy environment.

The biological edge detection model developed in this paper can be easily applied to practice and can steer the UCAV during target recognition, which will considerably increase the autonomy of the UCAV.

A hybrid model of saliency-based visual attention and ABC algorithm is proposed for biological edge detection. An improved ABC algorithm is applied in edge detection of the salient region.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

Communicating the identity of a family business generates positive results in consumer response. The paper aims to understanding how the efficient transmission of family identity can influence consumer behavior is essential for designing family firms' marketing communication strategies.

An experimental study based on the eye-tracking technique was designed to determine how attention to (familiar vs non-familiar) visual stimuli on a website influences consumer recognition of a family firm status and how it influences consumer behavior. A sample of 212 individuals was exposed to (simulated) websites of family and non-family firms in the hospitality industry to capture information about their eye movements and measure visual attention to specific stimuli that communicated family identity.

Visual attention has a direct and positive influence on recognizing family firm's identity (FFI). Through FFI, visual attention has an indirect positive effect on trust in the company and attitude toward the brand (BraAtt). Trust in a firm positively affects purchase intention (PurInt).

It is known that consumers can perceive a FFI; however, there is no study on the sensory mechanisms operating in consumers' perceptions of family identity. The study contributes to understanding how consumers can perceive a FFI. This study proposes a novel method for evaluating consumer responses by transmitting family business identity on digital platforms.

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.

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.

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.

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

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

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.

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.

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.

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

Graphic health warnings (GHWs) on tobacco product packaging constitute one component within a multifaceted set of tobacco control measures. This study aims to understand whether consumers’ attention to GHWs will be associated with recall and quit intentions, using Australia as the case for this study.

Using the 14 GHWs currently in market as visual stimuli, non-probability intercept sampling was conducted, eye tracking and post-survey datasets were collected from a total of 419 respondents across three Australian cities.

Results show the front graphic image areas draw initial attention and the Quitline message area holds the longest attention duration. Attention is highly correlated with better quality of recall of health warning information, emotive responses, believability ratings among smokers and smokers’ perception of health risks and quit intentions. Associations are also noted with perceived health risk and quitting intentions.

To the best of the authors’ knowledge, this is the first study that has objectively tested the effectiveness of in-market tobacco GHWs in Australia and highlights eye tracking as a valid measurement approach that can enhance and drive new insights to evaluate consumer behaviour towards visual stimuli. This study extends new knowledge around the physiological relationships between viewing behaviours, health vulnerability perceptions and intentions to quit smoking, which has theoretical implications for the extended parallel process model which underpins this research.

Dynamic advertising, including television and online video ads, demands new theory and tools developed to understand attention to moving stimuli. The purpose of this study is to empirically test the predictions of a new dynamic attention theory, Dynamic Human-Centred Communication Systems Theory, versus the predictions of salience theory.

An eye-tracking study used a sample of consumers to measure visual attention to potential areas of interest (AOIs) in a random selection of unfamiliar video ads. An eye-tracking software feature called intelligent bounding boxes (IBBs) was used to track attention to moving AOIs. AOIs were coded for the presence of static salience variables (size, brightness, colour and clutter) and dynamic attention theory dimensions (imminence, motivational relevance, task relevance and stability).

Static salience variables contributed 90% of explained variance in fixation and 57% in fixation duration. However, the data further supported the three-way interaction uniquely predicted by dynamic attention theory: between imminence (central vs peripheral), relevance (motivational or task relevant vs not) and stability (fleeting vs stable). The findings of this study indicate that viewers treat dynamic stimuli like real life, paying less attention to central, relevant and stable AOIs, which are available across time and space in the environment and so do not need to be memorised.

Despite the limitations of small samples of consumers and video ads, the results of this study demonstrate the potential of two relatively recent innovations, which have received limited emphasis in the marketing literature: dynamic attention theory and IBBs.

This study documents what does and does not attract attention to video advertising. What gets attention according to salience theory (e.g. central location) may not always get attention in dynamic advertising because of the effects of relevance and stability. To better understand how to execute video advertising to direct and retain attention to important AOIs, advertisers and advertising researchers are encouraged to use IBBs.

This study makes two original contributions: to marketing theory, by showing how dynamic attention theory can predict attention to video advertising better than salience theory, and to marketing research, showing the utility of tracking visual attention to moving objects in video advertising with IBBs, which appear underutilised in advertising research.