Recognition of Object Categories and Scenes

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.