Deep Learning Techniques for Scene Recognition

Deep learning has undoubtedly changed the face of machine learning & computer vision over the last few years, but in some ways, the research field has taken a retrograde step by focusing on simpler (and somewhat contrived) problems of image classification rather than of whole 'scene understanding'. The fundamental research of this PhD will look to reignite some of the older ideas in whole semantic scene understanding, and enrich these with new ideas, in the light of the advances made using deep learning.
Humans have the ability to quickly analyse and understand the meaning behind a situation deputed in an image or video-capture. This capacity is essential because it allows us to carry out different tasks in our every-day lives. We are able to recognize objects, features and textures, classify them correctly and then assign meaning to the whole scene. Although machines have made great progress in object recognition tasks, they still have a long way to go until they will be able to perform with human comparable efficiency and accuracy in scene recognition tasks. Unlike objects, scenes are represented by entire images, not just certain parts of it. Nowadays, high order semantic scene recognition is one of the most interesting topics in the visual computing field and we consider that training machines to recognize and tell the story behind an image can greatly benefit our lives. The importance of this research topic can be observed in areas such as surveillance, driving assistance and human-machine interaction.