Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing

Basim Azam; Ranju Mandal; Brijesh Verma

doi:10.1109/IJCNN52387.2021.9534310

Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing

Basim Azam
, Ranju Mandal
, Brijesh Verma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9534310
Publication status	Published - 18 Jul 2021
Externally published	Yes
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	18/07/21 → 22/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 14 Life Below Water

Keywords

deep learning
feature selection
Image parsing
semantic segmentation

Access to Document

10.1109/IJCNN52387.2021.9534310

Cite this

Azam, B., Mandal, R., & Verma, B. (2021). Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN52387.2021.9534310

@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",

}

Azam, B, Mandal, R & Verma, B 2021, Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9534310

Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing. / Azam, Basim; Mandal, Ranju; Verma, Brijesh.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing

AU - Azam, Basim

AU - Mandal, Ranju

AU - Verma, Brijesh

N1 - Funding Information: VI. ACKNOWLEDGMENT This research was supported under Australian Research Council’s Discovery Projects funding scheme (project number DP200102252). Publisher Copyright: © 2021 IEEE.

PY - 2021/7/18

Y1 - 2021/7/18

N2 - 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.

AB - 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.

KW - deep learning

KW - feature selection

KW - Image parsing

KW - semantic segmentation

UR - https://www.scopus.com/pages/publications/85116501073

U2 - 10.1109/IJCNN52387.2021.9534310

DO - 10.1109/IJCNN52387.2021.9534310

M3 - Conference contribution

AN - SCOPUS:85116501073

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Joint Conference on Neural Networks, IJCNN 2021

Y2 - 18 July 2021 through 22 July 2021

ER -

Relationship Aware Context Adaptive Feature Selection Framework for Image Parsing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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