Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing

• URL: http://arxiv.org/abs/2204.06214v1
• Date: Wed, 13 Apr 2022 07:35:39 GMT
• Title: Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing
• Authors: Ranju Mandal, Basim Azam, and Brijesh Verma
• Abstract summary: The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information. The experimental outcomes when evaluated on benchmark datasets are promising.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models have been efficient lately on image parsing tasks. However, deep learning models are not fully capable of exploiting visual and contextual information simultaneously. The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information. The novel idea here is to have a visual layer to learn visual characteristics from binary class-based learners, a contextual layer to learn context, and then an integration layer to learn from both via genetic algorithm-based optimal fusion to produce a final decision. The experimental outcomes when evaluated on benchmark datasets are promising. Further analysis shows that optimized network weights can improve performance and make stable predictions.

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