Cascade CNN Framework for Low Resolution Image Classification
By: Kannojia, Suresh Prasad
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Contributor(s): Jaiswal, Gaurav.
Publisher: New Delhi STM Journals 2019Edition: Vol 6 (1), Jan-Apr.Description: 39-43p.Subject(s): Computer EngineeringOnline resources: Click Here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: : Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of these super resolution images are fused with low resolution convolutional features which are extracted by low resolution CNN (LRCNN) feature extractor. A deep neural network classifier is trained on these fused CNN features. This classifier classifies low resolution images using learned fused features. Proposed cascade framework has been evaluated on different benchmark image dataset MNIST, CIFAR10 and achieves competitive accuracy results.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2021469 |
: Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of these super resolution images are fused with low resolution convolutional features which are extracted by low resolution CNN (LRCNN) feature extractor. A deep neural network classifier is trained on these fused CNN features. This classifier classifies low resolution images using learned fused features. Proposed cascade framework has been evaluated on different benchmark image dataset MNIST, CIFAR10 and achieves competitive accuracy results.
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