Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias

Hosseinkhani, Jila; Joslin, Chris

doi:10.1109/mipr.2019.00041

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…As the final step, similar to Chapter 3 (and our works in [156,157]), we employed a characteristic of the HVS to enhance the resulting saliency map known as eccentricity sensitivity [135] which emphasizes on the salient areas and removes their farther surrounding areas by blurring them.…”

Section: Saliency Map Estimation and Enhancementmentioning

confidence: 99%

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Hosseinkhani¹

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I have a deep debt of gratitude to everyone who supported me throughout my Ph.D. study. Firstly, I would like to express my sincere appreciation and thanks to my Ph.D. thesis supervisor, Professor Chris Joslin for his guidance, support, motivation, and knowledge. Thanks must also go to my committee members for their helpful suggestions and discussions. Besides, I am mostly grateful to my wonderful parents for their unconditional love, support, and faith in me. Words cannot express how grateful I am to my mother, father, and sister for their support and encouragements. This accomplishment would not have been achievable without their constant support and love. I also owe much to my caring and supportive friends in Canada and elsewhere who helped me stay strong and keep moving forward during these years. v Table of Contents Abstract iii Acknowledgments v Table of Contents vi List of Tables ix List of Figures x Acronyms xiv Dup-Diagonal of-45 degree upward Ddown+ Diagonal of +45 degree downward Ddown-Diagonal of-45 degree downward xv

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Section: Saliency Map Estimation and Enhancementmentioning

confidence: 99%

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Hosseinkhani¹

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A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

Hosseinkhani

Joslin

2019

International Journal of Multimedia Data Engineering and Management

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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.

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Saliency Priority Using Bottom-up Features for Static and Dynamic Scenes Without Cognitive Bias

Cited by 2 publications

References 19 publications

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

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