Enhancing Fashion Cloth Image Classification through Hybrid CNN-SVM Modeling:A Multi-Class Study

The classification of fashion cloth images is an important and challenging task in the field of computer vision. In recent years, deep learning (DL) techniques, especially Convolutional Neural Networks (CNNs), have shown remarkable performance in image classification tasks. The proposed study presents a hybrid model for the multi-classification of fashion cloth images by combining the strengths of both CNNs and SVM. Using binary classification, the authors first divide the fashion clothing photographs into male and female categories. Then, multi-classify the images into four categories, including ethnic, casual, formal, and sportswear. The 5000 images that make up the dataset for the study have been divided into training and testing sets. The proposed hybrid model combines the feature extraction capabilities of CNNs and the decision-making power of SVMs to produce improved classification results. The results of the experiments show that the binary classification results in an accuracy of 95.5%, while the multi-classification results in the best accuracy of 96.24% in the case of the formal class of fashion cloth.