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SA-Net: A deep spectral analysis network for image clustering
- Source :
- Neurocomputing. 383:10-23
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image clustering. In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering. While spectral analysis is established with solid theoretical foundations and has been widely applied to unsupervised data mining, its essential weakness lies in the fact that it is difficult to construct a proper affinity matrix and determine the involving Laplacian matrix for a given dataset. In this paper, we propose a SA-Net to overcome these weaknesses and achieve improved image clustering by extending the spectral analysis procedure into a deep learning framework with multiple layers. The SA-Net has the capability to learn deep representations and reveal deep correlations among data samples. Compared with the existing spectral analysis, the SA-Net achieves two advantages: (i) Given the fact that one spectral analysis procedure can only deal with one subset of the given dataset, our proposed SA-Net elegantly integrates multiple parallel and consecutive spectral analysis procedures together to enable interactive learning across different units towards a coordinated clustering model; (ii) Our SA-Net can identify the local similarities among different images at patch level and hence achieves a higher level of robustness against occlusions. Extensive experiments on a number of popular datasets support that our proposed SA-Net outperforms 11 benchmarks across a number of image clustering applications.<br />Comment: arXiv admin note: text overlap with arXiv:2009.05235
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
business.industry
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Cognitive Neuroscience
Deep learning
Computer Science - Computer Vision and Pattern Recognition
Pattern recognition
02 engineering and technology
Computer Science Applications
Interactive Learning
ComputingMethodologies_PATTERNRECOGNITION
020901 industrial engineering & automation
Artificial Intelligence
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Spectral analysis
Artificial intelligence
Laplacian matrix
business
Cluster analysis
Feature learning
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 383
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....6835192338fb108b125b9a0f532b2e58