A convolutional neural network-based platform for clothing style feature extraction and new style generation: integrating optical feature extraction for enhanced design

With the rapid advancements in computer vision and deep learning technologies, the fashion industry is progressively moving towards intelligent and personalized solutions. This paper presents a Convolutional Neural Network (CNN)-based platform for extracting clothing style features and generating new styles. The proposed platform leverages CNN models to effectively extract key features from clothing images, integrating optical feature extraction techniques to enhance the recognition of intricate design elements, such as fabric textures, patterns, and color schemes. Additionally, Generative Adversarial Networks (GANs) are employed to create innovative new styles, ensuring that the generated designs are both aesthetically pleasing and unique. The platform aims to enhance the efficiency and creativity of fashion design, catering to the market's demand for diverse and personalized clothing. Experimental results demonstrate that the designed platform excels in both feature extraction and new style generation, capable of producing high-quality, creative, and diverse clothing designs.