Incremental Learning of Object Categories

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Automatic object recognition in complex visual scenes is still one of the most active research areas in computer vision. However, the majority of currently available systems are only capable of solving very specific problems with a fixed number of known object classes (currently between 100 and 1000). This proposal overcomes these limitations of static systems by developing methods, which adapt a system initially trained from a fixed dataset by incrementally increasing its knowledge. We call this process of adapting a systems knowledge over time "incremental learning" or "lifelong learning". Such methods are essentially for recognizing and representing previously unseen categories, especially in the time of publicly available image collections on the world wide web such as Flickr. A long term goal of this project is to close the gap between human and machine vision, which can be done by building systems that are able to automatically detect new object categories and to add them to their current datasets. Adapting the current representation modalities will be one important topic involved within this process to ensure the separability of different categories even with thousands of known classes. In the first stage of the project, novelty detection and learning from few examples will be in the focus of research. In addition, methods for efficiently updating the current model need to be investigated, both on the level of features and on the level of object instances. Thereby, costly learning from scratch can be avoided. Furthermore, active learning is a topic highly relevant to ensure the relevance of new examples with respect to the resulting classification model. Since there is no common benchmark dataset within this area of research, we plan to build a dataset and to define evaluation criteria for lifelong learning scenarios which will be made publicly available. As an real-world application, we will evaluate the developed methods on a mobile robot, that will autonomously explore its environment and will incrementally extend its knowledge. The long term goal of this proposal is to develop methods, with which a system is able to automatically adapt itself to the daily changing world and to increase and correct its knowledge over time.