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Deep learning models have been very successful in computer vision and imageprocessing applications. Since its inception, Many top-performing methods forimage segmentation are based on deep CNN models. However, deep CNN models failto integrate global and local context alongside visual features despite havingcomplex multi-layer architectures. We propose a novel three-layered deeplearning model that assiminlate or learns independently global and localcontextual information alongside visual features. The novelty of the proposedmodel is that One-vs-All binary class-based learners are introduced to learnGenetic Algorithm (GA) optimized features in the visual layer, followed by thecontextual layer that learns global and local contexts of an image, and finallythe third layer integrates all the information optimally to obtain the finalclass label. Stanford Background and CamVid benchmark image parsing datasetswere used for our model evaluation, and our model shows promising results. Theempirical analysis reveals that optimized visual features with global and localcontextual information play a significant role to improve accuracy and producestable predictions comparable to state-of-the-art deep CNN models.

Deep Learning Model with GA based Feature Selection and Context Integration