Deep Learning Model with GA based Feature Selection and Context Integration

TL;DR

This paper introduces a novel three-layer deep learning model that integrates GA-optimized visual features with global and local contextual information for improved image segmentation accuracy.

Contribution

The paper presents a new three-layer deep learning architecture that separately learns and then combines visual features with global and local context using genetic algorithm optimization.

Findings

Model achieves promising results on Stanford Background and CamVid datasets.

Optimized features with context improve accuracy and stability.

Comparable performance to state-of-the-art deep CNN models.

Abstract

Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that assiminlate or learns independently global and local contextual information alongside visual features. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.