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キーワード:
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要旨:
We present an exemplar-based object recognition system which is capable of on-line learning of representations of scenes and objects from image sequences.
Local appearance features are used in a tracking framework to find `key-frames' of the input sequence during learning.
The representation of the stored sequences which are used for recognition of novel images consists only of the appearance features in these key-frames and contains no further a-priori assumptions about the underlying sequences.
The system is able to create sparse and extendable representations and shows good recognition performance
in a variety of viewing conditions for databases of natural and synthetic image sequences.
