Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142326
Title: Deep visual saliency on stereoscopic images
Authors: Nguyen, Anh-Duc
Kim, Jongyoo
Oh, Heeseok
Kim, Haksub
Lin, Weisi
Lee, Sanghoon
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Nguyen, A.-D., Kim, J., Oh, H., Kim, H., Lin, W., & Lee, S. (2019). Deep visual saliency on stereoscopic images. IEEE Transactions on Image Processing, 28(4), 1939-1953. doi:10.1109/TIP.2018.2879408
Journal: IEEE Transactions on Image Processing
Abstract: Visual saliency on stereoscopic 3D (S3D) images has been shown to be heavily influenced by image quality. Hence, this dependency is an important factor in image quality prediction, image restoration and discomfort reduction, but it is still very difficult to predict such a nonlinear relation in images. In addition, most algorithms specialized in detecting visual saliency on pristine images may unsurprisingly fail when facing distorted images. In this paper, we investigate a deep learning scheme named Deep Visual Saliency (DeepVS) to achieve a more accurate and reliable saliency predictor even in the presence of distortions. Since visual saliency is influenced by low-level features (contrast, luminance, and depth information) from a psychophysical point of view, we propose seven low-level features derived from S3D image pairs and utilize them in the context of deep learning to detect visual attention adaptively to human perception. During analysis, it turns out that the low-level features play a role to extract distortion and saliency information. To construct saliency predictors, we weight and model the human visual saliency through two different network architectures, a regression and a fully convolutional neural networks. Our results from thorough experiments confirm that the predicted saliency maps are up to 70% correlated with human gaze patterns, which emphasize the need for the hand-crafted features as input to deep neural networks in S3D saliency detection.
URI: https://hdl.handle.net/10356/142326
ISSN: 1057-7149
DOI: 10.1109/TIP.2018.2879408
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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