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.
@article{arxiv.2204.06214,
title = {Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing},
author = {Ranju Mandal and Basim Azam and Brijesh Verma},
journal= {arXiv preprint arXiv:2204.06214},
year = {2022}
}