TY - GEN
T1 - CompactKdt
T2 - 2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
AU - Aly, Mohamed
AU - Munich, Mario
AU - Perona, Pietro
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84860699404&partnerID=8YFLogxK
U2 - 10.1109/WACV.2012.6162995
DO - 10.1109/WACV.2012.6162995
M3 - Conference contribution
AN - SCOPUS:84860699404
SN - 9781467302333
T3 - Proceedings of IEEE Workshop on Applications of Computer Vision
SP - 505
EP - 512
BT - 2012 IEEE Workshop on the Applications of Computer Vision, WACV 2012
Y2 - 9 January 2012 through 11 January 2012
ER -