Robust Object Co-Segmentation Using Background Prior
Abstract
Given a set of images that contain objects from a common category, object co-segmentation aims at automatically discovering and segmenting such common objects from each image. During the past few years, object co-segmentation has received great attention in the computer vision community. However, the existing approaches are usually designed with misleading assumptions, unscalable priors, or subjective computational models, which do not have sufficient robustness for dealing with complex and unconstrained real-world image contents. This paper proposes a novel two-stage co-segmentation framework, mainly for addressing the robustness issue. In the proposed framework, we first introduce the concept of union background and use it to improve the robustness for suppressing the image backgrounds contained by the given image groups. Then, we also weaken the requirement for the strong prior knowledge by using the background prior instead. This can improve the robustness when scaling up for the unconstrained image contents. Based on the weak background prior, we propose a novel MR-SGS model, i.e., manifold ranking with the self-learned graph structure, which can infer suitable graph structures in a data-driven manner rather than building the fixed graph structure relying on the subjective design. Such capacity is critical for further improving the robustness in inferring the foreground/background probability of each image pixel. Comprehensive experiments and comparisons with other state-of-the-art approaches can demonstrate the effectiveness of the proposed work.
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CCA concentrates most information on a few top eigenvectors, thus unbalancing the projected data and lowering hash code quality drastically.
“However, similar to the conventional methods that reduce the feature dimensionality via projection schemes, CCA will also concentrate most information on a few top eigenvectors, thus unbalancing the projected data and lowering the hash code quality drastically [4].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [4]
The performance of hashing schemes may degrade significantly if the projected dimensions are imbalanced, i.e., their variances vary a lot.
“Unfortunately, it has been acknowledged that the performance of those hashing schemes may degrade significantly if the projected dimensions are imbalanced [4], [6], [13], i.e., their variances vary a lot.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [4]
IsoH may be unstable in large-scale and high-dimensional experiments due to overfitting.
“But the reconstruction on a small covariance matrix seems restricted, so it may be unstable in large-scale and high-dimensional experiments [4] due to the overfitting problem.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [4]
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- Authors
- Han, J., Quan, R., Zhang, D., & Nie, F.
- Affiliations
- Xidian University (CN)
- Northwestern Polytechnical University (CN)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-04
- Volume
- 27
- Issue
- 4
- Pages
- 1639-1651
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2773771410
- Cited by
- 169
- References
- 65
- Citations by year
- 2024: 2 · 2023: 4 · 2022: 6 · 2021: 21 · 2020: 20 · 2019: 47 · 2018: 69
- FWCI
- 15.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation of China (61473231)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- background prior, robustness, manifold ranking, two-stage framework
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