Section: Overall Objectives
LEAR's main focus is learning based approaches to visual object recognition and scene interpretation, particularly for object category detection, image retrieval, video indexing and the analysis of humans and their movements. Understanding the content of everyday images and videos is one of the fundamental challenges of computer vision and we believe that significant advances will be made over the next few years by combining state of the art image analysis tools with emerging machine learning and statistical modeling techniques.
LEAR's main research areas are:
Image features and descriptors and robust correspondence. Many efficient lighting and viewpoint invariant image descriptors are now available, such as affine-invariant interest points and histogram of oriented gradient appearance descriptors. Our research aims at extending these techniques to give better characterizations of visual object classes, for example based on 2D shape descriptors or 3D object category representations, and at defining more powerful measures for visual salience, similarity, correspondence and spatial relations.
Statistical modeling and machine learning for visual recognition. Our work on statistical modeling and machine learning is aimed mainly at making them more applicable to visual recognition. This includes both the selection, evaluation and adaptation of existing methods, and the development of new ones designed to take vision specific constraints into account. Particular challenges include: (i) the need to deal with the huge volumes of data that image and video collections contain; (ii) the need to handle “noisy” training data, i.e., to combine vision with textual data; and (iii) the need to capture enough domain information to allow generalization from just a few images rather than having to build large, carefully marked-up training databases.
Visual recognition. Visual recognition requires the construction of exploitable visual models of particular objects and of object and scene categories. Achieving good invariance to viewpoint, lighting, occlusion and background is challenging even for exactly known rigid objects, and these difficulties are compounded when reliable generalization across object categories is needed. Our research combines advanced image descriptors with learning to provide good invariance and generalization. Currently the selection and coupling of image descriptors and learning techniques is largely done by hand, and one significant challenge is the automation of this process, for example using automatic feature selection and statistically-based validation diagnostics.
Video interpretation. Humans and their activities are one of the most frequent and interesting subjects of videos, but also one of the hardest to analyze owing to the complexity of the human form, clothing and movements. Our research aims at developing robust visual shape descriptors to characterize humans and their movements with little or no manual modeling. Video, furthermore, permits to easily acquire large quantities of image data often associated with text. This data needs to be handled efficiently: we need to develop adequate data structures; text classification can help to select relevant parts of the video.