Alexandra Gilinsky (EE, Technion)
Computing distances between large sets of SIFT descriptors
is a basic step in numerous algorithms in computer vision.
When the number of descriptors is large, as is often the
case, computing these distances can be extremely time consuming.
In this research we propose the SIFTpack: a compact
way of storing SIFT descriptors, which enables significantly
faster calculations between sets of SIFTs than the current
solutions. SIFTpack can be used to represent SIFTs densely
extracted from a single image or sparsely from multiple different
images. We show that the SIFTpack representation
saves both storage space and run time, for both finding
nearest neighbors and for computing all distances between
all descriptors. The usefulness of SIFTpack is demonstrated
as an alternative implementation for K-means dictionaries
of visual words and for image retrieval.
MSc seminar under the supervision of Prof. Lihi Zelnik Manor
The seminar will be delivered in Hebrew.