Representation Models and Machine Learning Techniques for Scene Classificatio

Scene classification is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Humans are able to recognize complex visual scenes at a single glance, despite the number of objects with different poses, colors, shadows and textures that may be contained in the scenes. Understanding the robustness and rapidness of this human ability has been a focus of investigation in the cognitive sciences over many years. These studies have stimulated researches in computer vision in building artificial scene recognition systems. Motivations beyond that of pure scientific curiosity are provided by several important computer vision applications in which scene classification can be exploited (e.g., robot navigation systems). Different methods have been proposed to model and to describe the content of a scene. Different machine learning procedures have been employed to automatically learn commonalities and differences between different classes. In this chapter we survey some of the state of the art approaches for scene classification. For each approach we report a description and a discussion of the most relevant peculiarities.