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.
