Research on the optimization of interior design visual elements and art design innovation method under the deep convolutional network framework

Abstract

As data science and computer science continue to advance, the application of deep learning methods to address technological innovation and optimize interior design has emerged as a significant area of research. In this paper, we propose a VR matching algorithm that utilizes deep convolutional networks to match the appropriate visual elements of the room layout, thereby realizing a panoramic scheme for interior design. A method of automatic extraction of features and labels of room layout based on a convolutional neural network is designed, trained, and tested with the help of a convolutional deep confidence network, and the labeled room layout feature map is classified using the Softmax classifier. A fast retrieval algorithm is being investigated with input in the form of images and combining text and content. Firstly, we input the room layout feature image, extract the feature vector using a convolutional neural network model, and then calculate the similarity between the feature vector in the visual element decorative design drawing and the feature vector of the input room layout image. The maximum similarity is selected as the visual element design scheme of the feature, and then the picture of the panoramic scheme is realized in order to let the designer get the customer’s feedback on the design effect in a more timely manner. The classification models developed in this paper achieve an accuracy rate exceeding 90%, effectively fulfilling the task of classifying images of room layout features. The user’s satisfaction with the interior design methodology experience projects proposed in this paper is also all higher than 70%, and the feedback from both designers and users is very good, which shows the excellent effect of deep convolutional networks in the optimization of interior design visual elements.