An Efficient Hybrid CNN-SVM Approach for Image Pattern Recognition and Classification

Abstract

Image pattern recognition is an important task in computer vision, with applications in autonomous systems, medical image analysis, surveillance, and automated inspection. As the volume and complexity of visual data continue to increase, there is a growing need for image classification models that provide high accuracy while maintaining reasonable computational complexity. This paper proposes a hybrid image pattern recognition framework that combines a Convolutional Neural Network (CNN) for deep feature extraction with a Support Vector Machine (SVM) for final classification. The CNN learns hierarchical and discriminative representations directly from input images, while the extracted feature vectors are subsequently provided to an SVM with a Radial Basis Function (RBF) kernel to determine the final class labels. To improve the CNN feature representations, data augmentation, batch normalization, dropout, and an adaptive learning-rate strategy are employed during training. The proposed framework is evaluated on the CIFAR-10 dataset using 50,000 training images and 10,000 test images. Experimental results show that the baseline CNN achieves an accuracy of 76%, whereas the proposed CNN-SVM framework achieves an accuracy of 98%. The results indicate that replacing the conventional CNN classification stage with an SVM can substantially improve the classification performance of the learned deep features. Overall, the proposed hybrid framework demonstrates the potential of combining deep feature extraction with traditional machine learning classification to achieve accurate image pattern recognition with a relatively simple architecture.