Adaptive Vocabulary Forests for
Dynamic Indexing and Category Learning
Abstract
Histogram pyramid representations computed from a vocabulary tree
of visual words have proven valuable for a range of image
indexing and recognition tasks; however, they have only used a
single, fixed partition of feature space. We present a new
efficient algorithm to incrementally compute set-of-trees
(forest) vocabulary representations, and show they improve
recognition and indexing performance in methods which use
histogram pyramids. Our algorithm incrementally adapts a
vocabulary forest with an inverted filesystem at the leaf nodes
and automatically keeps existing histogram pyramid database
entries up-to-date in a forward filesystem. It is possible not
only to apply vocabulary tree indexing algorithms directly, but
also to compute pyramid match kernel values efficiently. On
dynamic recognition tasks where categories or objects under
consideration may change over time, we show that adaptive
vocabularies o ffer significant performance advantages when
compared to a single, fixed vocabulary.
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