Developing a multi-channel hybrid model for indoor scene recognition using text-based classifiers and convolutional neural networks

Indoor scene recognition is a computer vision problem used to identify different environments in enclosed spaces, such as offices, libraries, kitchens, and restaurants. It is an extensive and evolving research area, particularly in applications such as robotics, security, and assistive technologies for individuals with disabilities, where categorizing spaces provides contextual information about the environment. As in many computer vision problems, convolutional neural networks (CNNs) are predominantly used for indoor scene recognition. While CNNs achieve relatively high success in outdoor scene recognition by focusing on global features of an image (e.g., general outlines of mountains, seas, or skies), they do not perform as effectively in indoor scene recognition, where the emphasis is on local features such as furniture, objects, and various items. In this study, which utilizes the MIT 67 Indoor Scene dataset, a two-channel hybrid model is proposed. The model combines features extracted from a CNN with those obtained from a text-based model developed using object recognition words. By integrating natural language processing (NLP) and image processing techniques, the hybrid model improves the test accuracy of the image processing model by 9%, achieving a high-performance rate.