A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine

TL;DR

This paper introduces a hybrid CNN-AIS model that enhances image recognition performance on small datasets by integrating Artificial Immune System principles into CNN architecture.

Contribution

It presents a novel hybrid architecture combining CNN and AIS, specifically designed to improve recognition accuracy with limited training data.

Findings

Effective on small datasets like MNIST with limited data

Improved recognition accuracy over standard CNNs on small samples

Validated with personal data samples from two classes

Abstract

Machine learning methods are used today for most recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. The proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in size