## 1 Introduction  
By nature, every organism, including humans, can detect each and every stimulus rapidly, including emotions, danger, etc. Perceiving emotions is a natural and environmental ability for humans. Emotions are usually categorized as happy, sad, or neutral, depending on how humans feel and the information they perceive from their surroundings through observation [1]. Surrounding salience (also called saliency) depends upon the characteristics that make it stand out from its neighbours. A visually salient stimulus attracts attention. Saliency is the visual prominence of the scene. It typically arises from the contrast between an item and its surroundings.  
Attention modelling is mainly based on saliency maps in the field of computer science [2]. Saliency maps compute visually observable locations within the image based on the normal visual features that include brightness, colour, and motion [3]. Currently, many saliency models are available in the literature and the results of these models represent dominant visual locations of an image or video frames based upon three of the most common features of an image including the colour, intensity level, and image orientation. The models are created by the hierarchal decomposition of these features, which in the end combines them to form a saliency map. According to the data of eye movements,visual saliency does have an impact on eye movement, but the presence of an emotional object in a video scene reliably reduced the effect of visual salience [4]. However, initial eye fixation starts on highly emotional objects rather than neutral ones.  
Understanding a scene and focusing on its most salient object is the most significant ability of the human visual system [5]. Most of the salient regions are selected from a scene using visual attention, eliminating inadequate information and thus grabbing the useful information from the salient region [6]. It has been found that the movements of the eye and shifting of the attention commences towards the highest salience region and then pauses so that the attention can shift towards the next highest salience region [7]. Images or video frames with the highest salience guide eye movement and attention, and then inhibit it so that it can move to the next most salient location [8]. Properties of the stimulus or the images played an important role while guiding eye movements, which leads to the emergence of the salient location [9]. There is also a spatial information enhanced region contrast method and histogram-based contrast method which measures the saliency by assigning a pixel-wise saliency value which is based on the colour separation of an image pixel to produce a saliency map with a high resolution [10].  
Emotional content in the scene elicits a behavioural response in terms of attention capture [11]. Research has shown that despite any large motions in the visual scene, eye movements are mostly localized to the centre of the screen [12]. Therefore, the distance of the eye (fixations) from the centre is a good way to measure if the emotional content has evoked an eye fixation farther from the centre. In contrast, other metrics like the span or saccadic eye movement will not capture this behaviour.  
In this study, evidence of emotional salience associated with visual saliency model is explored. A bottom-up saliency model Graph-Based Visual Saliency (GBVS) was used in the study to construct a fully connected graph representing dissimilarity between the salient regions [13; 14]. According to the literature, the GBVS model more accurately predicts saliency maps than all other models, including the Itti-Koch model [15]. It assigns a higher value to the centre of the frame of the videos [16]. Every pixel or region in a video frame or an image represents a measured amount of human attention, thereby indicating visual salience. Predicting fixations in videos through bottom-up salience has been very successful [17; 18].