@inproceedings{b929aa25d3454700b34dcefa37d68df1,
title = "Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing",
abstract = "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.",
keywords = "deep learning, feature selection, Image parsing, semantic segmentation",
author = "Basim Azam and Ranju Mandal and Brijesh Verma",
note = "Funding Information: VI. ACKNOWLEDGMENT This research was supported under Australian Research Council{\textquoteright}s Discovery Projects funding scheme (project number DP200102252). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",
year = "2021",
month = jul,
day = "18",
doi = "10.1109/IJCNN52387.2021.9534310",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",
}
