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Asymmetric cyclical hashing for large scale image retrieval

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Asymmetric cyclical hashing for large scale image retrieval

  1. 1. 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
  2. 2. 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 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
  3. 3. 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 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:
  4. 4. 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 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.
  5. 5. 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 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.
  6. 6. 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 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
  7. 7. 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 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.
  8. 8. 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 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
  9. 9. 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 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.

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