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Classification of Low Quality Images Using Convolutional Neural Network and Deep Belief Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | El-Ashmony, E. El-Dosuky, Ma Elmougy, Samir |
| Copyright Year | 2016 |
| Abstract | Low quality images become more challenge and core problem in recent decade because of the ambiguity of contents of them. Convolutional deep neural networks are used for solving this problem. In this work, we used a combination of convolutional neural network and deep belief network to construct an efficient model able to classify low quality images. This model has the capability in extracting effective features from low quality images. Data augmentation is used through this model to increase the accuracy of the system. Scikit-Learn python library is used in implementation the system on STL-10 dataset. The results showed that the proposed model increase the accuracy of the system by 0.20%. |
| Starting Page | 19 |
| Ending Page | 28 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| DOI | 10.21608/ijicis.2016.19822 |
| Volume Number | 16 |
| Alternate Webpage(s) | https://ijicis.journals.ekb.eg/article_19822_24d5a5614030ffe40b5d10669cce52dc.pdf |
| Alternate Webpage(s) | https://doi.org/10.21608/ijicis.2016.19822 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
