Volume-3 , Issue-4 , Aug 2015, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Generalization of Determinant Kernels for Non-Square Matrix and its Application in Video Retrieval

  Mohammad Jafari, Neda Abdollahi, Ali Amiri, Mahmood Fathy

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.4 , pp.1-6, Jul-2015

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  Abstract
  For a specific set of features selected for representing videos, the performance of a content-based video retrieval system depends critically on the similarity or dissimilarity measures used. In this paper, we propose a kernel approach to improve the retrieval performance of content-based video retrieval systems namely determinant kernel. The input of this kernel is the dot product of feature matrices that extracted from shot visual information. Due to the variation in the number of each shot frames, the size of feature matrices are different and so the result of dot product become a non-square matrices. Almost all available techniques use summarizing methods to equalize the size of the matrices which lead to loss some parts of information. To solve this problem, we present a non-square determinant kernel based on Radic’s definition. We evaluate the performance of the derived Kernels by retrieving video shots of news and speaking videos. Experimental results confirm the effectiveness of our proposed algorithm.

  Key-Words / Index Term
  Content-based video retrieval; determinant kernel; non-square matrices; similarity measurement; feature matrix

  References
  [1] J. Yuan, H. Wang, L. Xiao, W. Zheng, J. Li, F. Lin, B. Zhang, “A Formal Study of Shot Boundary Detection,” IEEE Transaction on Circuits and Systems for Video Technology, vol. 17, no. 2, pp. 168-186, 2007.
  [2] W.M. Hu, N. Xie, L. Li, X. Zeng, S.J. Maybank, "A Survey on Visual Content-Based Video Indexing and Retrieval," SMC-C(41), no. 6, pp. 797-819, November 2011.
  [3] J. Yuan, H. Wang, L. Xiao, W. Zheng, J. Li, F. Lin, B. Zhang, “A formal study of shot boundary detection,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 2, pp. 168–186, 2007.
  [4] A. F. Smeaton, P. Over, A. R. Doherty, “Video shot boundary detection: Seven years of TRECVid activity,” Comput. Vis. Image Understanding, vol. 114, no. 4, pp. 411–418, 2010.
  [5] K. W. Sze, K. M. Lam, G. P. Qiu, “A new key frame representation for video segment retrieval,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 9, pp. 1148–1155, 2005.
  [6] B. T. Truong, S. Venkatesh, “Video abstraction: A systematic review and classification,” ACM Trans. Multimedia Comput., Commun. Appl., vol. 3, no. 1, art. 3, pp. 1–37, 2007.
  [7] D. Besiris, F. Fotopoulou, N. Laskaris, G. Economou, “Key frame extraction in video sequences: A vantage points approach,” in Proc. IEEE Workshop Multimedia Signal Process., Athens, Greece, pp. 434–437, 2007.
  [8] D. P. Mukherjee, S. K. Das, S. Saha, “Key frame estimation in video using randomness measure of feature point pattern,” IEEE Trans. Circuits Syst. Video Technol., vol. 7, no. 5, pp. 612–620, 2007.
  [9] R. Yan, A. G. Hauptmann, “A review of text and image retrieval approaches for broadcast news video,” Inform. Retrieval, vol. 10, pp. 445–484, 2007.
  [10] A. G. Hauptmann, R. Baron, M. Y. Chen, M. Christel, P. Duygulu, C. Huang, R. Jin, W. H. Lin, T. Ng, N. Moraveji, N. Papernick, C. Snoek, G. Tzanetakis, J. Yang, R. Yan, H. Wactlar, “Informedia at TRECVID 2003: Analyzing and searching broadcast news video,” in Proc. TREC Video Retrieval Eval., Gaithersburg,MD,2003.Available:http://www-nlpir.nist.gov/projects/tvpubs/tvpapers03/cmu.final.paper.pdf
  [11] E. Cooke, P. Ferguson, G. Gaughan, C. Gurrin, G. Jones, H. L. Borgue, H. Lee, S. Marlow , K. McDonald, M. McHugh, N. Murphy , N. O’Connor, N. O’Hare, S. Rothwell, A. Smeaton, P. Wilkins, “TRECVID 2004 experiments in Dublin city university,” in Proc. TREC Video Retrieval Eval., Gaithersburg, MD, 2004. Available: http://wwwnlpir.nist.gov/projects/tvpubs/tvpapers04 /dcu.pdf.
  [12] W. M. Hu, D. Xie, Z. Y. Fu, W. R. Zeng, S. Maybank, “Semantic based surveillance video retrieval,” IEEE Trans. Image Process., vol. 16, no. 4, pp. 1168–1181, 2007.
  [13] J. Sivic, A. Zisserman, “Video Google: Efficient visual search of videos,” in Toward Category-Level Object Recognition.. Berlin, Germany: Springer, pp. 127–144, 2006.
  [14] Y. Aytar, M. Shah, J. B. Luo, “Utilizing semantic word similarity measures for video retrieval,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1–8, 2008.
  [15] C.G.M. Snoek, M. Worring, “Multimodal video indexing: A review of the state-of-the-art,” Multimedia Tools Appl., vol. 25, no. 1, pp. 5–35, 2005.
  [16] P. Browne, A. F. Smeaton, “Video retrieval using dialogue, keyframe similarity and video objects,” in Proc. IEEE Int. Conf. Image Process., vol. 3, pp. 1208–1211, 2005.
  [17] C.G.M. Snoek, B. Huurnink, L. Hollink, M. de Rijke, G. Schreiber, M. Worring, “Adding semantics to detectors for video retrieval,” IEEE Trans. Multimedia, vol. 9, no. 5, pp. 975–985, 2007.
  [18] T. Volkmer, A. Natsev, “Exploring automatic query refinement for text-based video retrieval,” in Proc. IEEE Int. Conf. Multimedia Expo., Toronto, pp. 765–768, 2006.
  [19] A. Amir, W. Hsu, G. Iyengar, C. Y. Lin, M. Naphade, A. Natsev, C. Neti, H. J. Nock, J. R. Smith, B. L. Tseng, Y. Wu, D. Zhang, “IBM research TRECVID-2003 video retrieval system,” in Proc. TREC Video Retrieval Eval., Gaithersburg, MD, 2003. Available: http://wwwnlpir.nist.gov/projects/tvpubs/ tvpapers03 /ibm.smith.paper.final2.pdf
  [20] H. Sahbi, “Kernel PCA for similarity in variant shape recognition,” Journal of Neuro Computing, vol. 70, pp. 3034–3045. , 2006
  [21] P.H. Gosselin, M. Cord, S. Philipp-Foliguet, “Combining visual dictionary, kernel-Based similarity and learning strategy for image category retrieval,” Special issue on Similarity matching in computer vision and multimedia, Computer Vision and Image Understanding, vol. 110, no. 3, pp. 403–417, 2008.
  [22] A. Nasser, D. Hamad, J. L. Rouas, S. Ambellouis, “The use of kernel methods for audio events detection,” International Conference on Information and Communication Technologies: From Theory to Applications, pp.1–6, 2008.
  [23] Y. Chen, J.Z. Wang, “Image categorization by learning and reasoning with regions,” International Journal on Machine Learning Research, vol. 5, pp. 913–939, 2004.
  [24] R. Hoi, S.C.H. Jin, M.R. Lyu, “Learning non parametric kernel matrices from pair wise constraints,” 24th International Conference on Machine Learning (ICML’07), pp. 361–368, 2007.
  [25] D.Y. Yeung, H. Chang, “A Kernel Approach for semi-supervised metric learning,” IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 141–149, 2007.
  [26] D.Y. Yeung, H. Chang, G. Dai, “A scalable kernel-based semi-supervised metric learning algorithm with out-of-sample generation ability,” Neural Computation, vol. 20, no. 1, pp. 2839–2861, 2008.
  [27] B. Scholkopf, A. Smola, K.R. Muller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural Computation, vol. 10, pp. 1299–1319, 1998.
  [28] G. Baudat, F. Anouar, “Generalized discriminate analysis using a kernel approach,” Neural Computation, vol. 12, pp. 2385–2404, 2000.
  [29] F. Perronnin, C.R. Dance, “Fisher kernels on visual vocabularies for image categorization,” IEEE Conference on Computer Vision & Pattern Recognition, 2007.
  [30] S.K. Zhou, ‘Trace and determinant kernels between matrices’, SCR technical report, 2004.
  [31] M. Radic, ‘A definition of the determinant of a rectangular matrix’, Glasnik Mat, vol. 1, no. 21, pp. 17-22, 1966.
  [32] A. Amiri, M. Fathy, “Hierarchical Keyframe-based Video summarization Using QR Decomposition and modified k-means clustering,” EURASIP Journal on Advances in Signal Processing, Article ID 892124, 16 Pages, 2010.
  [33] NIST, “Homepage of Trecvid Evaluation,” http://www-nlpir.nist.gov/projects/trecvid/.
  [34] A. Amiri, M. Fathy, “Video shot boundary detection using QR-Decomposition and Gaussian transition detection,” EURASIP Journal on Advances in Signal Processing, 12 pages, 2009.
  [35] A. Amiri, N. Abdollahi, M. Jafari, M. Fathy, “Hierarchical Key-Frame Based Video Shot Clustering Using Generalized Trace,” Journal of Innovative Computing Technology, Springer-Verlag Berlin Heidlberg, pp. 251-257, 2011.
  [36] N. Abdollahi, M. Jafari, M. Bayat, A. Amiri, M. Fathy, “An Efficient Parallel Algorithm for Computing Determinant of Non-Square Matrices Based on Radic’s Definition,” International Journal of Distributed and Parallel Systems (IJDPS) Vol.6, No.4, 2015.
  

  Citation
  Mohammad Jafari, Neda Abdollahi, Ali Amiri, Mahmood Fathy, "Generalization of Determinant Kernels for Non-Square Matrix and its Application in Video Retrieval," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.4, pp.1-6, 2015

• Open Access   Article

  Realization of FIR Filter using Distributed Arithmetic Architecture

  Chandan Singh

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.4 , pp.7-12, Jul-2015

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  Abstract
  This work represents an efficient implementation of Finite Impulse response (FIR) filter using Distributed Arithmetic (DA) architecture. Distributed Arithmetic (DA) architecture is an efficient technique for calculation of inner product or multiply and accumulate (MAC) using Look-up tables. In absence of DA, the direct method of implementing MAC uses dedicated multipliers which are fast but consume considerable hardware. MAC operation is common in digital signal processing algorithms. Here DA replaces the explicit multiplications by ROM look-up tables which is an efficient technique to implement on FPGA. To save the memory units, we have divided the look-up tables. Area saving from using DA can be up to 80% in DSP hardware design. This work shows the implementation of an eight order FIR filter using DA architecture through VHDL coding and the code is finally simulated using Modelsim and then compared with MATLAB output.

  Key-Words / Index Term
  Distributed Arithmetic (DA) architecture, VHDL. MATLAB, FIR Filter

  References
  [1]. Ramesh.R, Nathiya.R “Realization of FIR filter using Modified Distributed Arithmetic Architecture” An internation journal Vol 3. No. 1, February 2012.
  [2]. Yajun Zhou ; Sch. of Autom., HangZhou Dianzi Univ., Hangzhou, China ; Pingzheng Shi “Distributed Arithmetic for FIR Filter implementation on FPGA” IEEE 2011 International Conference on Multimedia Technology (ICMT), pp 294 - 297July 2011 Print ISBN: 978-1-61284-771-9, DOI: 10.1109/ICMT.2011.6003032
  [3]. Sang Yoon Park, Pramod Kumar Meher “Low-Power, High-Throughput, and Low-Area Adaptive FIR Filter Based on Distributed Arithmetic” IEEE Transactions on circuits and systems-II: Express briefs, Vol. 60, N0. 6, pp. 346-350 June 2013.
  [4]. M Surya Prakash, Rafi Ahamed Shaik “Low-Area & High-Throughput Architecture for an Adaptive filter using Distributed Arithmetic” IEEE Transaction on Circuit & System, Vol. 60, No. 11, pp. 781-785, Nov. 2013
  

  Citation
  Chandan Singh, "Realization of FIR Filter using Distributed Arithmetic Architecture," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.4, pp.7-12, 2015

• Open Access   Article

  Construction of Cryptographic e-Tags using Chronological Binary Transforms

  Monday O. Eze and Chekwube G. Nwankwo

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.4 , pp.13-20, Jul-2015

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  Abstract
  A cryptographic tag generator is a computational algorithm designed and implemented to synthesize e-tags for system identification. Some of the digital resources that could be tagged are data files, digital images, video clips, and so on. As a best practice procedure, the tags are usually encrypted before insertion into the internal structure of the identified object, and also decrypted whenever there is a need to read the tags. This research presents a new algorithm for cryptographic tag generation using a series of chronological transformations. The dataset for the experimental run consists of 200 standard identification tags that could be applied for securing the patients’ registration data in a typical hospital information system. The scope of this research covers the synthesis of the tags cum implementation of the cryptographic system, but not the insertion of the cipher tags into the internal structure of the host media. This research is important for a number of reasons. One is that it contributes to the emerging computational field of evolving hybrid technologies that combine steganographic and cryptographic techniques in system security.

  Key-Words / Index Term
  Cryptographic Tags, Computational Algorithm, Cryptographic Array, Cryptanalysis, Steganography

  References
  [1] O.C. Abikoye, A.G.Akintola, and S. Ibrahim, “Design and Implementation of a Yoruba Language Cryptosystem”, Ilorin Journal of Sc. Vol 1, No. 1, 2014, pp50-60
  [2] www.oxforddictionaries.com, Accessed Jul. 10, 2015.
  [3] G. Durfee, “Cryptanalysis of RSA Using Algebraic and Lattice Methods”, A PhD Dissertation submitted to the Dept. of Computer Science Stanford University, June 2002..
  [4] M. Ahmed, T. M. Shahriar Sazzad and M. E. Mollah, "Cryptography and State-of-the-art Techniques ", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012, pp583-586
  [5] E. Mansoor, S. Khan, and U.B. Khalid. "Symmetric Algorithm Survey: A Comparative Analysis", International Journal of Computer Applications, Vol. 61, No.20, Jan. 2013 pp12-19
  [6] K.T. Huang, Y.N. Lin, and J.H. Chiu. Real-time Mode Hopping of Block Cipher Algorithms for Mobile Streaming. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 2, April 2013, pp127-142
  [7] R. Masram, V. Shahare, J. Abraham and R. Moona. Analysis and Comparison of Symmetric Key Cryptographic Algorithms Based on Various File Features, International Journal of Network Security & Its Applications (IJNSA), Vol.6, No.4, July 2014 , pp43-51
  [8] P.T. Kenekayoro. One way functions and public key cryptography, African Journal of Mathematics and Computer Science Research, Vol. 4, No 6, June 2011, pp 213-216.
  [9] T. Cormen, C. Leiserson, R. Rivest and C. Stein. Introduction to Algorithms (3rd Ed), The MIT Press, Massachusetts, 2009
  [10] T. Hidema and K. Toshinobu. “An Expansion Algorithm for Higher Order Differential Cryptanalysis of Secret Key Ciphers “, Journal of the National Institute of Information and Communications Technology Vol.52 Nos.1/2, 2005, pp119-128
  [11] F. Massacci and L. Marraro,"Logical Cryptanalysis as a SAT Problem: Encoding and Analysis of the U.S. Data Encryption Standard",Journal of Automated Reasoning Vol 24, Issue 1-2, 2000, pp165–203.
  [12] H. Zodpe, P. Wani and R. Mehta. "Hardware Implementation of Algorithm for Cryptanalysis", International Journal on Cryptography and Information Security (IJCIS), Vol.3, No.1, March 2013, pp7-15.
  [13] K.R. Shah and B. Gambhava, "New Approach of Data Encryption Standard Algorithm", International Journal of Soft Computing and Engineering (IJSCE) Vol. 2, Issue-1, March 2012, pp322-325.
  [14] N. Nithin, A. Bongale and G. P. Hegde."Image Encryption based on FEAL Algorithm", International Journal of Advances in Computer Science and Technology, Vol. 2, No.3, March 2013, pp14-20.
  [15] K.Mohan, S.E.Neelakandan. "Secured Robust Video Data Hiding Using Symmetric Encryption Algorithms", International Journal of Innovative Research in Engineering & Science, Vol 6, Issue 1, Dec 2012, pp17-29.
  [16] P. Garg, S. Varshney, and M. Bhardwaj. "Cryptanalysis of Simplified Data Encryption Standard Using Genetic Algorithm", American Journal of Networks and Communications. Vol. 4, No. 3, 2015, pp. 32-36.
  [17] K. Alallayah, M. Amin, W. Abdelwahed and A. Alaa. “Applying Neural Network for Simplified Data Encryption Standard (SDES) Cipher System Cryptanalysis”. The International Arab Journal of Infor. Tech. Vol. 9, No.2, March 2012, pp163-169.
  [18] G. Poonam, "Cryptanalysis of SDES via Evolutionary Computation Techniques", International Journal of Computer Science and Information Security, Vol. 1, No. 1, May 2009, pp1-7
  [19] D. Zhu, "Security Control in Inter-Bank Fund Transfer", Journal of Electronic Commerce Research, VOL. 3, NO. 1, 2002, pp15-22.
  [20] S. Murdoch and R. Anderson."Security Protocols and Evidence: Where Many Payment Systems Fail", In Proceeding of Financial Cryptography and Data Security, Barbados, March 3-7, 2014, pp1-12
  [21] B. Joshi and A. Khandelwal., "Clustering with Data Encryption in Wireless Communication: A Critical Survey", International Journal of Advanced Computer Research, Vol. 4 Number-3 Issue-16, Sep.2014, pp813-818
  [22] J. Vagle, "Furtive Encryption: Power, Trust, and the Constitutional Cost of Collective Surveillance", Indiana Law Journal, Vol. 90:101, 2015, pp101-150.
  [23] J. Voldman, M. L. Gray, M. Toner, and M. A. Schmidt., “A Microfabrication-Based Dynamic Array Cytometer”, Analytical Chem., 2002, 74 (16), July 10, 2002, pp 3984–3990.
  [24] A. Singh and U. Jauhari, “Data Security by Preprocessing the Text with Secret Hiding”, Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.3, May 2012, pp 63-74.
  [25] K. I. Rahmani, A. Arora and P. Naina. "A Crypto-Steganography: A Survey", International Journal of Advanced Computer Science and Applications(IJACSA), Vol. 5, No. 7, 2014, pp149-155.
  

  Citation
  Monday O. Eze and Chekwube G. Nwankwo, "Construction of Cryptographic e-Tags using Chronological Binary Transforms," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.4, pp.13-20, 2015