  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.
