Asymmetric cyclical hashing for large scale image retrieval

OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT
Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com
ASYMMETRIC CYCLICAL HASHING FOR LARGE SCALE IMAGE RETRIEVAL
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
CERTIFICATE
Certified that this project report titled “Asymmetric Cyclical Hashing for Large Scale Image
Retrieval” is the bonafide work of Mr. _____________Who carried out the research under my
supervision Certified further, that to the best of my knowledge the work reported herein does not
form part of any other project report or dissertation on the basis of which a degree or award was
conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
DECLARATION
I hereby declare that the project work entitled “Asymmetric Cyclical Hashing for Large Scale
Image Retrieval” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the
requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record
of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the
best of my knowledge, the work reported here is not a part of any other thesis or work on the basis
of which a degree or award was conferred on an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
ACKNOWLEDGEMENT
I am extremely glad to present my project “Asymmetric Cyclical Hashing for Large Scale
Image Retrieval” which is a part of my curriculum of third semester Master of Science in
Computer science. I take this opportunity to express my sincere gratitude to those who helped me
in bringing out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
ABSTRACT:
This paper addresses a problem in the hashing technique for large scale image retrieval:
learn a compact hash code to reduce the storage cost with performance comparable to that of the
long hash code. A longer hash code yields a better precision rate of retrieved images. However it
also requires a larger storage, which limits the number of stored images. Current hashing methods
employ the same code length for both queries and stored images. We propose a new hashing
scheme using two hash codes with different lengths for queries and stored images, i.e., the
Asymmetric Cyclical Hashing. A compact hash code is used to reduce the storage requirement,
while a long hash code is used for the query image. The image retrieval is performed by computing
the Hamming distance of the long hash code of the query and the cyclically concatenated compact
hash code of the stored image to yield a high precision and recall rate. Experiments on
benchmarking databases consisting up to one million images show the effectiveness of the
proposed method.

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
INTRODUCTION:
With the explosive growth of both the number of images available on the Internet and the
dimensionality of image descriptors, two problems must be addressed: retrieval speed and storage
cost. On the issue of speed, hashing methods have a sublinear time complexity for solving large
scale content based image retrieval problems. The computation of Hamming distances between
hash codes of the query and the stored images is very fast and can be accomplished with a simple
data structure and efficient bit operations. Even the exhaustive computation of millions of
Hamming distances could take only a second using a single CPU.
Many hashing methods have been proposed in recent years for better retrieval performance.
They can be divided into two major categories: random projection based and data-dependent based
methods. The Locality Sensitive Hashing (LSH) is a very first and popular random projection
based hashing method with a strong theoretical foundation.The major drawback of the LSH and
its variants (e.g. the SKLSH) is the requirement of a long hash code to achieve a high precision for
the similarity estimation. It leads to a large storage cost and even slow hard disk access for large
scale databases. To deal with this problem, datadependent based methods are proposed to find
compact hash codes by finding the mapping from the high dimensional real-valued vectors
describing images to the low dimensional hash codes. These data-dependent methods achieve
satisfactory performances with short hash codes, but they fail to improve performances when the
number of bits increases as the LSH and its variants do. Recently, many sophisticated data-
dependent hashing methods , have shown better performances by using longer hash codes.
However, the storage cost of a long hash code for a large scale database is very large and
it limits the number of images being stored in memory. When the memory cannot accommodate
hash codes for all images, frequent access to hard disks or a distributed system will be needed
which is much slower than direct memory access . As a result, queries may collapse because of a
long response time which severely limits the application of hashing methods with long codes for
large scale databases. For an image retrieval system processing millions of query in a second on
the Internet, a slow access of image hash codes costs much more time in comparison to the fast
Hamming distance computation. To address this problem created by using the same code length
for both the query and stored images, we propose the Asymmetric Cyclical Hashing (ACH). The
major contribution of the ACH is to use a short hash code with k bits storage for each stored image

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
and mk bits for the query to provide a good retrieval performance by using a long hash code for
the similarity computation, where k and m are non-negative integers. In contrast, the asymmetric
hashing presented in , uses two different hash codes with the same length and thus still suffers
from the aforementioned limitation. We select mk bits for the query for ease of Hamming distance
computation. In other words, the ACH computes the Hamming distances between the mk-bit hash
code of the query and the m-times repetitive concatenation of the k-bit hash code of the stored
images.
Let x be the descriptor vector of an image and n = mk. Figure 1 provides a visual illustration of
the ACH. The ACH uses F (x) = [f1 (x), f2 (x), · · · , fn (x)]T : Rd → {−1, +1} n to generate the
n-bit hash codes for the query image as all hashing methods do. In contrast, for each image in the
database, the ACH uses G (x) = [g1 (x), g2 (x), · · · , gk (x)]T : Rd → {−1, +1} k to generate a k-
bit hash code B. During the query execution, B is repeated m times and the resulting mkbit codes
are concatenated to form an n-bit hash code for computing the Hamming distances between the
query and the stored images. Therefore, the storage requirement for each image is the short k-bit
code, while long mk-bit codes are used for the similarity computation during the query execution.
This paper is organized as follows. Section 2 briefly introduces related works while the ACH is
proposed in Section 3. Experimental results are presented and discussed in Section 4. Section 5
concludes this work.

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
CONCLUSION:
In this work, the Asymmetric Cyclical Hashing (ACH) is proposed to yield both high
similarity preservation among images and better storage efficiency. The ACH stores only k-bit
hash codes for the stored images, while it uses mk-bit hash codes to compute the similarity between
the stored images and the query. Experimental results show that the ACH yields higher precision
and recall rates in comparison to current hashing methods using the same storage cost. The ACH
is an unsupervised hashing method. Although it yields a certain degree of semantic preserving
capability as shown in our experiments, it heavily depends on the semantic similarity preservation
of the features being used.
A semi-supervised ACH is expected to yield a better retrieval results for semantic image
retrieval problems. On the other hand, the weighted Hamming distance has been shown to yield a
better precision in comparison to the traditional binary Hamming distance at the cost of more
computation. One of our future works is to extend the ACH to a real-valued weighted Hamming
distance based asymmetric hashing scheme to provide a higher similarity precision. One may
consider reducing both the storage and the computational costs of the real-valued weighed
Hamming distance based hashing. Another improvement of the ACH is to relax the restriction on
the multiple relationships between lengths of the short (k) and the long (mk) hash codes. However,
this also requires a more complicated optimization to find the long code based on the short code.
Bit selection and weighting may be required in the optimization

CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111
ECWAY TECHNOLOGIES
REFERENCES:
[1] A. Gordo, F. Perronnin, Y. Gong, and S. Lazebnik, “Asymmetric distances for binary
embeddings,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp.
33–47, 2014.
[2] J. Wang, S. Kumar, and S.-F. Chang, “Semi-supervised hashing for large-scale search,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 12, pp. 2393–2406, 2012.
[3] B. Neyshabur, N. Srebro, R. Salakhutdinov, Y. Makarychev, and P. Yadollahpour, “The power
of asymmetry in binary hashing,” in NIPS, 2013, pp. 2823–2831.
[4] Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean
approach to learning binary codes for large-scale image retrieval,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2916–2929, 2013.
[5] M. Norouzi, A. Punjani, and D. J. Fleet, “Fast search in hamming space with multi-index
hashing,” in IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012, pp.
3108–3115.
[6] M. Raginsky and S. Lazebnik, “Locality-sensitive binary codes from shift-invariant kernels.”
in NIPS, vol. 22, 2009, pp. 1509–1517.
[7] B. Kulis and K. Grauman, “Kernelized locality-sensitive hashing for scalable image search,”
in IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009, pp. 2130–2137.

Asymmetric cyclical hashing for large scale image retrieval

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Empfohlen

Empfohlen (20)

Asymmetric cyclical hashing for large scale image retrieval