Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models

Hosseinkhani, Jila; Joslin, Chris

doi:10.4018/ijssci.2018100101

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…mean and variance. Before learning the probability functions of the extracted maps explained in Sections 4.2.1 and 4.2.2, the feature maps are weighted and normalized based on the results of our empirical study [133,134] indicating that the significance of color feature compared to the texture features were estimated as 60% versus 40% in our designed test and dataset. Then, both the prior and likelihood probabilities were learned from the extracted feature maps and targeted feature ranges resulting from our experiment [132].…”

Section: Saliency Map Estimation and Enhancementmentioning

confidence: 99%

See 1 more Smart Citation

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Hosseinkhani¹

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Saliency Map Estimation and Enhancementmentioning

confidence: 99%

“…First, the feature maps were weighted and normalized based on the results of our empirical study [132][133][134] indicating that the significance of color, texture, and motion features with respect to each other. Next, the probability functions of the extracted feature maps were learned.…”

Section: Saliency Map Estimation and Enhancementmentioning

confidence: 99%

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Hosseinkhani¹

Self Cite

View full text Add to dashboard Cite

show abstract

Computer-aided autism diagnosis based on visual attention models using eye tracking

Oliveira

Franco

Revers

et al. 2021

Sci Rep

View full text Add to dashboard Cite

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

show abstract

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

Hosseinkhani

Joslin

2019

International Journal of Multimedia Data Engineering and Management

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models

Cited by 3 publications

References 15 publications

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Computer-aided autism diagnosis based on visual attention models using eye tracking

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

Contact Info

Product

Resources

About