Brain Cognition Inspired Dual Pathway Cnn Architecture For Image Classification

A Novel Convolutional Neural Network Inspired by the Human Visual System

Introduction

The human visual system is a remarkable feat of engineering, capable of processing vast amounts of visual information with incredible speed and accuracy. Inspired by this biological marvel, researchers have developed convolutional neural networks (CNNs) - a class of deep learning algorithms - that have revolutionized the field of computer vision.

Global-Local Information Processing

One key aspect of the human visual system is its ability to process information at both a global and local level. Global processing allows us to perceive the overall structure of a scene, while local processing provides details about individual objects and their relationships.

CNNs leverage this hierarchical processing mechanism by organizing their layers into a cascade of convolution and pooling operations. Convolutional layers extract local features from the input, while pooling layers combine these features into increasingly global representations.

CogNet: A Novel Architecture

Inspired by the global-local processing of the human visual system, we propose CogNet - a novel CNN architecture. CogNet employs a series of "global-local" blocks (GL blocks), each of which consists of a convolutional layer followed by a pooling layer. These blocks are stacked sequentially to create a deep, hierarchical network.

Advantages of CogNet

CogNet offers several advantages over traditional CNN architectures:

* Improved Performance: CogNet's hierarchical processing enables it to extract rich features at multiple scales, resulting in improved accuracy on image classification and object detection tasks. * Interpretability: The layered architecture of CogNet makes it easier to understand the network's decision-making process, facilitating its use in applications where interpretability is crucial. * Scalability: CogNet's modular design allows for easy customization, enabling it to be scaled up or down depending on the size and complexity of the dataset.

Conclusion

CogNet is a novel CNN architecture inspired by the global-local information processing mechanism of the human visual system. Its hierarchical structure provides improved performance, interpretability, and scalability, making it a promising tool for a wide range of computer vision applications.