Saliency Detection based on Depth and Sparse Features

Gangbiao Chen, Chun Yuan


In this paper, we modified the region-based Human Visual System (HVS) model by import two features, sparse feature and depth feature. The input image is firstly divided into small regions. Then the contrast, sparse and depth feature of each region are extracted. We calculate the center-surround feature differences for saliency detection. In this step, the center shift method is adopted. In the weighting step, the human visual acuity is adopted. Compared with the existing related algorithms, experimental results on a large public database show that the modified method works better and can obtain a more accurate result.


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Paper Citation

in Harvard Style

Chen G. and Yuan C. (2015). Saliency Detection based on Depth and Sparse Features . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 441-446. DOI: 10.5220/0005292604410446

in Bibtex Style

author={Gangbiao Chen and Chun Yuan},
title={Saliency Detection based on Depth and Sparse Features},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},

in EndNote Style

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Saliency Detection based on Depth and Sparse Features
SN - 978-989-758-089-5
AU - Chen G.
AU - Yuan C.
PY - 2015
SP - 441
EP - 446
DO - 10.5220/0005292604410446