A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

Jila Hosseinkhani and Chris Joslin
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Jila Hosseinkhani: Carleton University, Ottawa, Canada
Chris Joslin: Carleton University, Ottawa, Canada

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2019, vol. 10, issue 2, 1-20

Abstract: In this article, the authors used saliency detection for video streaming problem to be able to transmit regions of video frames in a ranked manner based on their importance. The authors designed an empirically-based study to investigate bottom-up features to achieve a ranking system stating the saliency priority. We introduced a gradual saliency detection model using a Bayesian framework for static scenes under conditions that we had no cognitive bias. To extract color saliency, we used a new feature contrast in Lab color space as well as a k-nearest neighbor search based on k-d tree search technique to assign a ranking system into different colors according to our empirical study. To find the salient textured regions we employed contrast-based Gabor energy features and then we added a new feature as intensity variance map. We merged different feature maps and classified saliency maps using a Naive Bayesian Network to prioritize the saliency across a frame. The main goal of this work is to create the ability to assign a saliency priority for the entirety of a video frame rather than simply extracting a salient area which is widely performed.

Date: 2019
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