Advanced Scene Recognition with Digital Image Processing and Machine Learning

This paper presents a comprehensive scene recognition system implemented in Python using Digital Image Processing (DIP) techniques. Scene recognition is a fundamental problem in computer vision with numerous real-world applications, including autonomous navigation, surveillance, and augmented reality. Our system leverages DIP algorithms to extract and analyse key features from input images, enabling it to classify scenes into predefined categories accurately. The core of the system comprises a combination of feature extraction, feature representation, and machine learning components. We demonstrate the effectiveness of our approach through extensive experimentation on benchmark datasets, achieving state-of-the-art accuracy and showcasing the system's robustness in handling diverse environmental conditions and scenes. Additionally, the open-source implementation allows for customization and further research in the field of scene recognition and computer vision (CV).