Supervised Image Classification Using Deep Convolutional Wavelets Network
| Autor: | Mourad Zaied, Ridha Ejbali, Salima Hassairi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Contextual image classification
business.industry Computer science Deep learning Pattern recognition Linear classifier Machine learning computer.software_genre Convolutional neural network Deep belief network ComputingMethodologies_PATTERNRECOGNITION Wavelet Artificial intelligence business computer Classifier (UML) |
| Zdroj: | ICTAI |
| DOI: | 10.1109/ictai.2015.49 |
| Popis: | This paper gives a review of the deep learning history and proposes a new approach to supervised image classification by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a convolutional deep neural wavelet network. This network is obtained using a series of stacked auto-encoders and a linear classifier. The experimental test of our approach performed on "COIL-100" dataset demonstrates that our model is remarkably efficient for image classification compared to a known classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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