Deep Learning Techniques for Scene Recognition

场景识别的深度学习技术

• 批准号: 2114734
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2114734
• 关键词: Deep; Learning; Techniques; 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.

在过去的几年里，深度学习无疑改变了机器学习和计算机视觉的面貌，但在某些方面，该研究领域已经倒退了一步，专注于图像分类的更简单(或多或少是人为的)问题，而不是整个场景理解。这个博士的基础研究将着眼于重新点燃整个语义场景理解中的一些旧想法，并根据深度学习所取得的进展，用新的想法来丰富这些想法。人类有能力快速分析和理解图像或视频捕获中所代表的情况背后的含义。这种能力是必不可少的，因为它使我们能够在日常生活中执行不同的任务。我们能够识别物体、特征和纹理，对它们进行正确的分类，然后为整个场景赋予意义。虽然机器在物体识别任务上取得了很大的进步，但要想在场景识别任务中像人类一样高效和准确地执行任务，还有很长的路要走。与物体不同，场景是由整个图像来表示的，而不仅仅是其中的某些部分。如今，高阶语义场景识别是视觉计算领域最有趣的话题之一，我们认为训练机器来识别和讲述图像背后的故事可以极大地造福我们的生活。这一研究课题的重要性可以在监控、驾驶辅助和人机交互等领域观察到。