Design An Advanced Model for Image Classification Based on Neural Network Architecture: An Implementation

Abstract

In the field of machine learning and pattern recognition, object classification has garnered significant interest due to its wide-ranging applications, including visual surveillance. Despite the emergence of numerous deep learning-based methods for object classification, persistent challenges continue to affect overall classification accuracy. Complex backgrounds, crowded scenarios, and object similarity remain among the key obstacles. To address these challenges, we propose a technique that combines deep convolutional neural networks (DCNNs) with scale-invariant feature transform (SIFT). Initially, an enhanced saliency method is employed to identify relevant points, from which features are extracted.