# Deep Learning Model with GA based Feature Selection and Context Integration

arXiv: https://arxiv.org/abs/2204.06189 · HTML: https://mlsanity.com/paper/2204.06189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [UPSNet: A Unified Panoptic Segmentation Network](https://mlsanity.com/paper/1901.03784/) (2019)
- [OCNet: Object Context Network for Scene Parsing](https://mlsanity.com/paper/1809.00916/) (2018)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [Semantic Segmentation using Adversarial Networks](https://mlsanity.com/paper/1611.08408/) (2016)
- [Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks](https://mlsanity.com/paper/2003.05128/) (2020)
- [DeeperLab: Single-Shot Image Parser](https://mlsanity.com/paper/1902.05093/) (2019)
- [Efficient Uncertainty Estimation for Semantic Segmentation in Videos](https://mlsanity.com/paper/1807.11037/) (2018)
- [Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing](https://mlsanity.com/paper/1904.06400/) (2019)
- [Deep Spatio-Temporal Random Fields for Efficient Video Segmentation](https://mlsanity.com/paper/1807.03148/) (2018)
- [Deep Hierarchical Parsing for Semantic Segmentation](https://mlsanity.com/paper/1503.02725/) (2015)
- [A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification](https://mlsanity.com/paper/1704.08818/) (2017)

## Abstract (arXiv)

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.