Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/2392
Title: The effect of eye movements in response to different types of scenes using a graph-based visual saliency algorithm
Authors: Wahid, M.
Waris, A.
Gilani, S. O.
Subramanian, R.
Keywords: ANOVA (analysis of variance)
GBVS (graph-based visual saliency)
LSD (least significance difference)
Issue Date: 13-Aug-2021
Abstract: Saliency is the quality of an object that makes it stands out from neighbouring items and grabs viewer attention. Regarding image processing, it refers to the pixel or group of pixels that stand out in an image or a video clip and capture the attention of the viewer. Our eye movements are usually guided by saliency while inspecting a scene. Rapid detection of emotive stimuli an ability possessed by humans. Visual objects in a scene are also emotionally salient. As different images and clips can elicit different emotional responses in a viewer such as happiness or sadness, there is a need to measure these emotions along with visual saliency. This study was conducted to determine whether the existing available visual saliency models can also measure emotional saliency. A classical Graph-Based Visual Saliency (GBVS) model is used in the study. Results show that there is low saliency or salient features in sad movies with at least a significant difference of 0.05 between happy and sad videos as well as a large mean difference of 76.57 and 57.0, hence making these videos less emotionally salient. However, overall visual content does not capture emotional salience. The applied Graph-Based Visual Saliency model notably identified happy emotions but could not analyze sad emotions.
URI: http://localhost:8080/xmlui/handle/123456789/2392
Appears in Collections:Year-2019

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