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Journal for Studies in Management and Planning
Available at
http://edupediapublications.org/journals/index.php/JSMaP/
e-ISSN: 2395-0463
Volume 02 Issue 12
December 2016
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 49
To Show the Multiple Alignment of the Imagesearch Hash
Efficiency
1Thalari Vinay,2
. Mr.P.V.Vara Prasad Rao
1PG Scholar, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village, Hayathnagar
Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana
2Assosciate Professor, Department of ECE, SLC's Institute of Engineering and Technology, Piglipur Village,
Hayathnagar Mandal, Near Ramoji Film City, Ranga Reddy District, Hyderabad, Telangana
Abstract NETWORKS
Hashing is a popular and efficient method
for nearest neighbor search in large-scale
data spaces, by embedding high- dimensional feature descriptors into a
similarity-preserving Hamming space with a
low dimension. For most hashing methods,
the performance of retrieval heavily depends
on the choice of the high-dimensional
feature descriptor. Furthermore, a single
type of feature cannot be descriptive enough
for different images when it is used for
hashing. Thus, how to combine multiple
representations for learning effective
hashing functions is an imminent task. In
this paper, we present a novel unsupervised
Multiview Alignment Hashing (MAH)
approach based on Regularized Kernel
Nonnegative Matrix Factorization
(RKNMF), which can find a compact
representation uncovering the hidden
semantics and simultaneously respecting the
joint probability distribution of data.
Specifically, we aim to seek a matrix
factorization to effectively fuse the multiple
information sources meanwhile discarding
the feature redundancy. Since the raised
problem is regarded as nonconvex and
discrete, our objective function is then
optimized via an alternate way with
relaxation and converges to a locally
optimal solution. After finding the
low-dimensional representation, the hashing
functions are finally obtained through
multivariable logistic regression. The
proposed method is systematically evaluated
on three datasets: Caltech- and the results
show that our method significantly
outperforms the state-of-the-art multiview
hashing techniques.
I. INTRODUCTION
LEARNING discriminative embedding has
been a critical problem in many fields of
information processing and analysis, such as
object recognition image/video retrieval and
visual detection Among them, scalable
retrieval of similar visual information is
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Journal for Studies in Management and Planning
Available at
http://edupediapublications.org/journals/index.php/JSMaP/
e-ISSN: 2395-0463
Volume 02 Issue 12
December 2016
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 50
attractive, since with the advances of
computer technologies and the development
of the World Wide Web, a huge amount of
digital data has been generated and applied.
The most basic but essential scheme for
similarity search is the nearest neighbor
(NN) search: given a query image, to find an
image that is most similar to it within a large
database and assign the same label of the
nearest neighbor to this query image. NN
search is regarded as a linear search scheme
(O(N)), which is not scalable due to the
large sample size in datasets of practical
applications. Later, to overcome this kind of
computational complexity problem, some
tree-based search schemes are proposed to
partition the data space via various tree
structures. Among them, KD-tree and R-tree
[6] are successfully applied to index the data
for fast query responses. However, these
methods cannot operate with
high-dimensional data and do not guarantee
faster search compared to the linear scan. In
fact, most of the vision-based tasks suffer
from the curse of dimensionality problems1,
because visual descriptors usually have
hundreds or even thousands of dimensions.
Thus, some hashing schemes are proposed
to effectively embed data from a high- dimensional feature space into a similarity- preserving low-dimensional Hamming space
where an approximate nearest neighbor of a
given query can be found with sub-linear
time complexity. One of the most well- known hashing techniques that preserve
similarity information is Locality-Sensitive
Hashing (LSH) [7]. LSH simply employs
random linear projections (followed by
random thresholding) to map data points
close in a Euclidean space to similar codes.
Spectral Hashing (SpH) [8] is a
representative unsupervised hashing method,
in which the Laplace-Beltrami
eigenfunctions of manifolds are used to
determine binary codes. Moreover,
principled linear projections like PCA
Hashing (PCAH) [9] has been suggested for
better quantization rather than random
projection hashing.
Besides, another popular hashing approach,
Anchor Graphs Hashing (AGH) [10], is
proposed to learn compact binary codes via
tractable low-rank adjacency matrices. AGH
allows constant time hashing of a new data
point by extrapolating graph Laplacian
eigenvectors to eigenfunctions. More
relevant hashing methods can be However,
single-view hashing is the main topic on
which the previous exploration of hashing
methods focuses. In their architectures, only
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Journal for Studies in Management and Planning
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e-ISSN: 2395-0463
Volume 02 Issue 12
December 2016
Available online: http://edupediapublications.org/journals/index.php/JSMaP/ P a g e | 51
one type of feature descriptor is used for
learning hashing functions. In practice, to
make a more comprehensive description,
objects/images are always represented via
several different kinds of features and each
of them has its own characteristics. Thus, it
is desirable to incorporate
these heterogenous feature descriptors into
learning hashing functions, leading to multi- view hashing approaches. Multiview
learning techniques have been well explored
in the past few years and widely applied to
visual information fusion. Recently, a
number of multiview hashing methods have
been proposed for efficient similarity search,
such as Multi-View Anchor Graph Hashing
(MVAGH) [21], Sequential Update for
Multi-View Spectral Hashing (SU-MVSH)
[22], Multi-View Hashing (MVH-CS) 1The
effectiveness and efficiency of these
methods drop exponentially as the
dimensionality increases, which is
commonly referred to as the curse of
dimensionality Composite Hashing with
Multiple Information Sources (CHMIS) [24]
and Deep Multi-view Hashing (DMVH)
[25]. These methods mainly depend on
spectral, graph or deep learning techniques
to achieve data structure preserving
encoding. Nevertheless, the hashing purely
with the above schemes
are usually sensitive to data noise and
suffering from the high computational
complexity. The above drawbacks of prior
work motivate us to propose a novel
unsupervised mulitiview hashing approach,
termed Multiview Alignment Hashing
(MAH), which can effectively fuse multiple
information sources and exploit the
discriminative low-dimensional embedding
via Nonnegative Matrix Factorization
(NMF) . NMF is a popular method in data
mining tasks including clustering,
collaborative filtering, outlier detection, etc.
Unlike other embedding methods with
positive and negative values, NMF seeks to
learn a nonnegative partsbased
representation that gives better visual
interpretation of factoring matrices for high- dimensional data. Therefore, in many cases,
NMF may be more suitable for
subspacelearning tasks, because it provides a
non-global basis set which intuitively
contains the localized parts of objects
[26].In addition, since the flexibility of
matrix factorization can handle widely
varying data distributions, NMF enables
more robust subspace learning. More
importantly, NMF decomposes an original
