 Description
We present a novel algorithm, Compact Kd-Trees (CompactKdt),
that achieves state-of-the-art performance in
searching large scale object image collections. The algorithm
uses an order of magnitude less storage and computations
by making use of both the full local features (e.g.
SIFT) and their compact binary signatures to build and
search the K-Tree. We compare classical PCA dimensionality
reduction to three methods for generating compact binary
representations for the features: Spectral Hashing,
Locality Sensitive Hashing, and Locality Sensitive Binary
Codes. CompactKdt achieves significant performance gain
over using the binary signatures alone, and comparable
performance to using the full features alone. Finally, our
experiments show significantly better performance than the
state-of-the-art Bag of Words (BoW) methods with equivalent
or less storage and computational cost.
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