Adaptive Hashing with Sparse Modification for Scalable Image Retrieval

lifang zhang

Abstract:

By representing data through compact binary encodings, hashing techniques
have been widely used for data retrieval
because of their large data storage and efficient computational
time. Most hashing algorithms typically learn a finite number of
data projections, but learning optimal projections remains unaddressed.
To deal with this limitation, a novel approach, dubbed
Adaptive Hashing with Sparse Modification(AHSM), is proposed
that learns binary indices composed of the vertices of hypercube
and the projection matrix are comprised of two matrices in this
paper. The first matrix is orthogonal for rotating data and the
second one is sparse for modifying data. The essence of our scheme
is that an optimal transformation of data is learned to minimize
quantization distortion and improve model fidelity. AHSM has
two contributions: the first one is accuracy improvement, and the
second one is no increase on computational complexity. Through
experiments, we find that AHSM substantially surpasses several
state-of-the-art hashing schemes on three data sets.