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Combining pretrained CNN feature extractors to enhance clustering of complex natural images
- Source :
- Neurocomputing. 423:551-571
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most research, the CNN architecture for feature extraction is chosen arbitrarily, without justification. This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC). First, extensive experiments are conducted and show that, for a given dataset, the choice of the CNN architecture for feature extraction has a huge impact on the final clustering. These experiments also demonstrate that proper extractor selection for a given IC task is difficult. To solve this issue, we propose to rephrase the IC problem as a multi-view clustering (MVC) problem that considers features extracted from different architectures as different "views" of the same data. This approach is based on the assumption that information contained in the different CNN may be complementary, even when pretrained on the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. This approach is tested on nine natural image datasets, and produces state-of-the-art results for IC.<br />Comment: 21 pages, 16 figures, 10 tables, preprint of our paper published in Neurocomputing
- Subjects :
- Computer Science - Machine Learning
0209 industrial biotechnology
Point (typography)
Contextual image classification
business.industry
Computer science
Cognitive Neuroscience
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
Pattern recognition
02 engineering and technology
Convolutional neural network
Computer Science Applications
Image (mathematics)
Task (computing)
020901 industrial engineering & automation
Artificial Intelligence
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Cluster analysis
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 423
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....ec1c0b48022eee774bfc4390d3367408