A Comprehensive Hybrid Approach for Indoor Scene Recognition Combining CNNs and Text-Based Features

Abstract

Highlights What are the main findings? Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier. Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%. What is the implication of the main finding? Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability. Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Highlights What are the main findings? Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier. Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%. What is the implication of the main finding? Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability. Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Abstract Indoor scene recognition is a computer vision task that identifies various indoor environments, such as offices, libraries, kitchens, and restaurants. This research area is particularly significant for applications in robotics, security, and assistance for individuals with disabilities, as it enables the categorization of spaces and the provision of contextual information. Convolutional Neural Networks (CNNs) are commonly employed in this field. While CNNs perform well in outdoor scene recognition by focusing on global features such as mountains and skies, they often struggle with indoor scenes, where local features like furniture and objects are more critical. In this study, the "MIT 67 Indoor Scene" dataset is used to extract and combine features from both a CNN and a text-based model utilizing object recognition outputs, resulting in a two-channel hybrid model. The experimental results demonstrate that this hybrid approach, which integrates natural language processing and image processing techniques, improves the test accuracy of the image processing model by 8.3%, achieving a notable success rate. Furthermore, this study offers contributions to new application areas in remote sensing, particularly in indoor scene understanding and indoor mapping.