Three things everyone should know to improve object retrieval
Relja Arandjelovi´c and Andrew Zisserman
In: CVPR 2012, 18-20 June 2012., Rhode Island.
The objective of this work is object retrieval in large
scale image datasets, where the object is speciﬁed by an
image query and retrieval should be immediate at run time
in the manner of Video Google .
We make the following three contributions: (i) a new method to compare SIFT descriptors (RootSIFT) which
yields superior performance without increasing processing or storage requirements; (ii) a novel method for query
expansion where a richer model for the query is learnt
discriminatively in a form suited to immediate retrieval
through efﬁcient use of the inverted index; (iii) an improvement of the image augmentation method proposed by Turcot and Lowe , where only the augmenting features which are spatially consistent with the augmented image are kept.
We evaluate these three methods over a number of standard benchmark datasets (Oxford Buildings 5k and 105k,
and Paris 6k) and demonstrate substantial improvements
in retrieval performance whilst maintaining immediate retrieval speeds. Combining these complementary methods achieves a new state-of-the-art performance on these
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Sunando Sengupta|
|Deposited On:||15 June 2012|