Recognition of Object Categories and Scenes

该研究项目提出了先进的图像识别技术，以能够识别真实和无约束的室内外环境中的大量场景和对象类别，即交通场景(汽车、自行车、行人、人脸、街道标志等)、城市和自然场景(建筑物、景观等)。具有各种刚性和铰接式对象以及纹理。如今，几乎每个人都有一台数码相机，拍照或拍摄短片从未像现在这样容易。广播公司在每一次重大新闻发布会后都会收到来自公众的数千张图片，这些文件的注释是手动完成的。犯罪调查人员收集了大量的视觉证据，其分类也是手动完成的。英国拥有欧洲最多的安全摄像头，但摄像头提供的数据很少被挖掘。此外，视觉信息的识别和解释是自主智能机器人的主要要求之一。因此，迫切需要一种能够对大量可视文档进行自动分类和注释的可靠识别系统。为实现这一目标，即自动确定专家浏览文件的优先次序，任何成功都将被视为提高工作效率的明显好处。为了实现这一项目的目标，必须在特征提取、类别表示和高效搜索领域取得重大进展。最近的基于兴趣点的方法证明了在视觉识别的背景下处理大量类别的能力。这些方法展示了成功的场景和物体认知的方向。基于这些结果，我们建议开发新的技术来提取对背景杂波和视点变化具有稳健性的图像特征，这是当前图像识别领域面临的巨大挑战。这些特征将适用于各种外观和结构级别的场景和对象的同时表示以及对象的分割。中层图像分割方法有可能提供这样的特征，并可以在类别识别的背景下弥合兴趣点检测器和语义分割之间的差距。虽然我们的目标是同时解决这两个问题，但识别和分割领域几乎没有重叠。我们还建议引入新的层次表示法，它将利用新特征的特性，并允许有效地处理大量的图像类别。该演示将对不同图像属性(即，强度、颜色和纹理)以及不同对象部分和视图之间的关系的多个层次中的类别进行建模。多层次结构将允许基于与查询相关的图像线索从粗略到精细的分类。在这方面所做的工作很少，拟议中的研究可以为图像呈现问题提供新的线索。最后，利用高效的树结构和最近邻搜索技术来处理多类别学习中的海量数据，开发出新颖、高效和健壮的技术，为基本识别问题提供成功的解决方案，并在特征提取、类别表示和数据探索方面取得进展，使该项目具有很强的挑战性和冒险性。该项目预计在36个月内实现目标，将有一名研究生、一名研究助理和首席调查员参与。

This research project proposes to advance state-of-the-art image recognition techniques to be able to recognize a large number ofscenes and object categories in real and unconstrained indoor andoutdoor environments i.e. traffic scenes (cars, bicycle vehicles,pedestrians, human faces, street signs etc.), urban and naturalscenes (buildings, landscapes etc.) with various rigid andarticulated objects as well as textures. Nowadaysalmost everybody carries a digital camera and taking a photo or ashort video has never been easier. Broadcasting companies receivethousands of pictures from the general public after every majorevent and the annotation of those documents is done manually. Crime investigators collect large amounts ofvisual evidence and its classification is also done manually. The UKhas the largest number of security cameras in Europe but the dataprovided by the cameras is very little explored. Furthermore,recognition and interpretation of visual information is one of themajor requirements for autonomous intelligent robots. There is therefore a dire need for a reliable recognition system capable of automatic classification and annotation of large amounts of visual documents. Any success towards achieving that goal i.e., automatic prioritizing of document browsing for experts, will be seen as a clear benefit in improvingthe efficiency of work.To fulfil the objectives of this project major progress has to bemade in the domain of features extraction, category representationand efficient search. Recent interest point based approachesdemonstrate the capability of dealing with large numbers ofcategories in the context of visual recognition. These methods showpromising directions towards successful scene and objectrecognition. Based on these results we propose to develop noveltechniques for extracting image features robust to backgroundclutter and viewpoint change, which are currently great challengesin image recognition domain. Those features will be suitable forsimultaneous representation of scenes and objects at variousappearance and structure levels as well as for segmentation ofobjects. Mid-level image segmentation methods have a potential toprovide such features and can bridge the gap between interest pointdetectors and semantic segmentation in the context of categoryrecognition. There has been little overlap between recognition andsegmentation domains although the goal is to solve both problemssimultaneously.We also propose to introduce novel hierarchical representationswhich will exploit the properties of new features and allow to dealefficiently with large number of image categories. Therepresentation will model the categories in multiple hierarchies ofvarious image attributes i.e., intensity, color and texture as wellas relations between different object parts and views. The multiplehierarchies will allow for coarse-to-fine classification based onimage cues relevant to the query. Very little work has been done inthis area and the proposed research can shed new light on imagerepresentation problems. Finally, efficient tree structures andnearest neighbor search techniques will be employed to handle largeamounts of data in multi-category learning.Developing novel, efficient and robust techniques which may providesuccessful solutions to fundamental recognition problems and advancethe state-of-the-art in feature extraction, categoryrepresentation and data exploration, make this project verychallenging and adventurous. The project is expected to achieve theobjectives within 36 months and it will involve a research student,a research assistant and the principal investigator.