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A Multi-scale colour and Keypoint Density-based Approach for Visual Saliency Detection.

Bruno, A., Gugliuzza, F., Pirrone, R. and Ardizzone, R., 2020. A Multi-scale colour and Keypoint Density-based Approach for Visual Saliency Detection. IEEE Access, 8, 121330 - 121343.

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DOI: 10.1109/ACCESS.2020.3006700


In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

Item Type:Article
Additional Information:Funding Agency: OBIND Project; 10.13039/501100009887-Regione Siciliana; Azione;
Uncontrolled Keywords:eye-movements; interest points; saliency map; visual attention
Group:Faculty of Media & Communication
ID Code:34246
Deposited By: Symplectic RT2
Deposited On:06 Jul 2020 11:17
Last Modified:14 Mar 2022 14:23


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