Interior Design Idea Generation Using Image Classification

This paper proposes a new approach for personal- ized generating ideas of interior designs of an image classification technique with the assistance of machine learning. The proposed system takes an analysis of the design inspirations or user- uploaded images and classifies them into discrete interior design styles, such as modern, contemporary, traditional, and so on. Utilizing Convolutional Neural Networks (CNNs), the transfer learning using pre-trained models increases the accuracy and efficiency of classification for gaining knowledge from large image datasets. After classification, custom-made design recom- mendations like choosing furniture, color palettes, and layout suggestions will be generated based on a user’s design interest. This integration, using advanced machine learning techniques, will create a ’smart, automated design process’ that will help users to develop personalized interior spaces without requiring professional design proficiency. The entire research should help bring about a scalable, user-friendly platform for democratizing the interior design process, providing a simple arena for digital design ideas to be explored and adopted according to personal tastes or inspirations.