Feature selection for deep learning architectures is one of the important and challenging steps in developing an efficient image parsing application. In this paper, a novel image parsing architecture which makes use of unique feature selection is proposed. It introduces the idea of weighted relationship awareness to reduce the redundancy of features and optimally select an efficient subset of feature representations. The proposed architecture is evaluated on Cam Vid benchmark dataset. A comparison with state-of-the-art methods was conducted which showed significant improvements in terms of segmentation and classification accuracy.
|Title of host publication
|IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Published - 18 Jul 2021
|2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 2021 → 22 Jul 2021
|Proceedings of the International Joint Conference on Neural Networks
|2021 International Joint Conference on Neural Networks, IJCNN 2021
|18/07/21 → 22/07/21
- deep learning
- feature selection
- Image parsing
- semantic segmentation