A Study on Color Image Classification Technique for Websites Based on Improved CNN and SVM

With the explosive growth of image data, how to automatically and efficiently classify images is a central problem in computer vision. Deep learning-based image classification techniques, especially convolutional neural networks (CNNs), have demonstrated excellent performance in image classification tasks. However, although CNNs are strong in feature extraction, they may still be limited in classification accuracy by factors such as data volume and network structure. In this paper, we propose to combine CNN with support vector machine (SVM) to make full use of the feature extraction capability of CNN and the classification advantage of SVM to improve the accuracy of color image classification. This paper demonstrates the superiority of the method through theoretical derivation, construction of the combination algorithm, and experimental verification.