research-article

Estimation of Local and Global Superpixel Covariance for Salient Object Detection in Low Contrast Images

Authors:

Xiaolong ZhangAuthors Info & Claims

ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

Pages 314 - 319

https://doi.org/10.1145/3055635.3056574

Published: 24 February 2017 Publication History

Abstract

Salient object detection has become a hot topic in computer vision as it can substantially facilitate a wide range of applications. Conventional salient object detection models primarily rely on low-level image features, which may face great difficulties in low lighting scenarios. This paper proposes to estimate the saliency of low contrast images via covariance features. The input image is firstly decomposed into superpixel regions to estimate their covariances. Then, the local and global image saliency can be calculated using the covariance features respectively. Finally, a graph-based diffusion process is performed to refine the saliency maps. Extensive experiments have been conducted to evaluate the performance of the proposed model against eleven state-of-the-art models on five benchmark datasets and a nighttime image dataset.

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Cited By

Zhang ZGuo JYue HWang Y(2023)Global guidance-based integration network for salient object detection in low-light imagesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10386295(103862)Online publication date: Sep-2023
https://doi.org/10.1016/j.jvcir.2023.103862
Guo TXu X(2021)Salient object detection from low contrast images based on local contrast enhancing and non-local feature learningThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01964-937:8(2069-2081)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00371-020-01964-9

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ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

February 2017

545 pages

ISBN:9781450348171

DOI:10.1145/3055635

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

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Published: 24 February 2017

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ICMLC 2017

ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing

February 24 - 26, 2017

Singapore, Singapore

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Cited By

Zhang ZGuo JYue HWang Y(2023)Global guidance-based integration network for salient object detection in low-light imagesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10386295(103862)Online publication date: Sep-2023
https://doi.org/10.1016/j.jvcir.2023.103862
Guo TXu X(2021)Salient object detection from low contrast images based on local contrast enhancing and non-local feature learningThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-020-01964-937:8(2069-2081)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00371-020-01964-9

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