An efficient voice based information retrieval using bag of words based indexing
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2018-06-08 https://doi.org/10.14419/ijet.v7i2.33.14850 -
Information Retrieval System, Data Mining, Bag of Words, Data Base Maintenance. -
Abstract
Data mining is one of the leading and drastically growing researches nowadays. One of the main areas in data mining is Information Retrieval (IR). Information retrieval is a broad job and it is finding information without any structured nature. Infor-mation retrieval retrieves the user required information from a large collection of data. The existing approaches yet to improve the accuracy in terms of relevant accuracy. In this paper, it is motivated to provide an Information Retrieval System (IRS) where it can retrieve information with high relevancy. The proposed IRS is specially designed for physically challenged people like blind people where the input and the output taken/given is voice. The functionality of proposed IRS consists of three stages such as: (i) Voice to Text input, (II). Pattern Matching, and (III). Text to Voice output.In order to improve the accuracy and relevancy the proposed IRS uses an indexing method called Bag of Words (BOW). BOW is like an index-table which can be referred to store, compare and retrieve the information speedily and accurately. Index-table utilization in IRS improves the accuracy with minimized computational complexity. The proposed IRS is simulated in DOTNET software and the results are compared with the existing system results in order to evaluate the performance.
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References
[1] Lakra, Sachin, et al. "Application of fuzzy mathematics to speech-to-text conversion by elimination of paralinguistic content." arXiv preprint arXiv: 1209.4535 (2012).
[2] KNUTH, D., J. MORRIS, and V. PRATT. 1977. "Fast Pattern Matching in Strings." SIAM J on Computing, 6, 323-50.
[3] BOYER, R., and S. MOORE. 1977. "A Fast String Searching Algorithm." CACM, 20, 762-72.
[4] Ondrej Chum, James Philbin, Josef Sivic, Michael Isard, and Andrew Zisserman. Total recall:Automatic query expansion with a generative feature model for object retrieval. In ICCV, pages1–8, 2007.
[5] HHerv´eJ´egou, MatthijsDouze, and CordeliaSchmid. Improving bag-of-features for largescale image search. International Journal of Computer Vision, 87(3):316–336, 2010.
[6] James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Objectretrieval with large vocabularies and fast spatial matching. In CVPR, 2007. http://www.mysqltutorial.org/mysql-sample-database.aspx.
[7] Kleber, Florian, Markus Diem, and Robert Sablatnig, "Form classification and retrieval using bag of words with shape features of line structures"-IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, 2013.
[8] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. Open Information Extraction from the Web. In IJCAI, volume 7, pages 2670–2676, 2007.
[9] M. J. Cafarella, J. Madhavan, and A. Halevy. Web-Scale Extraction of Structured Data. ACM SIGMOD Record, 37(4):55–61, 2009.
[10] S. Cucerzan. Large-Scale Named Entity Disambiguation Based on Wikipedia Data. In EMNLP-CoNLL, volume 7, pages 708–716, 2007.
[11] T. Lin, P. Pantel, M. Gamon, A. Kannan, and A. Fuxman. Active Objects: Actions for Entity-Centric Search. In WWW, pages 589–598, 2012.
[12] J. M. Ponte and W. B. Croft. A Language Modeling Approach to Information Retrieval. In SIGIR, pages 275–281, 1998.
[13] J. Pound, P. Mika, and H. Zaragoza. Ad-hoc Object Retrieval in the Web of Data. In WWW, pages 771–780, 2010.
[14] S. E. Robertson and S. Walker. Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval. In SIGIR, pages 232–241, 1994.
[15] G. Salton, A. Wong, and C.-S. Yang.A Vector Space Model for Automatic Indexing.Communications of the ACM, 18(11):613–620, 1975.
[16] Kherfi, M.L., Ziou, D. and Bernardi, A. (2004) Image Retrieval from the World Wide Web: Issues, Techniques, andSystems. ACM Computing Surveys, 36, 35-67. http://dx.doi.org/10.1145/1013208.1013210.
[17] Datta, R., Joshi, D., Li, J. and Wang, J.Z. (2008) Image Retrieval: Ideas, Influences, and Trends of the NEW Age.ACM Computing Surveys, 40, 1-60.
[18] Yang, M., Kpalma, K. and Ronsin, J. (2010) A Survey of Shape Feature Extraction Techniques. Pattern Recognition,1-38.
[19] PenattiOtavio, A.B., Valle, E. and Torres, R.da.S. (2012) Comparative Study of Global Color and Texture Descriptorsfor Web Image Retrieval.Int. J. Via.Commun. Image R, 359-380.
[20] Deselaers, T., Keysers, D. and Ney, H. (2008) Features for Image Retrieval: An Experimental Comparison. InformationRetrieval, 11, 77-107.
[21] Mallat, S.G. (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactionson Pattern Analysis and Machine Intelligence, 11, 674-693.
[22] Sarck, J.L., Murtagh, F.D. and Bijaoui, A. (1998) Image Processing and Data Analysis: The Multiscale Approach.
[23] Hill, P., Achim, A. and Bull, D. (2012) The Undecimated Dual Tree Complex Wavelet Transform and Its Applicationto Bivariate Image Denoising Using a Cauchy Model. 19th IEEE International Conference on Image Processing (ICIP), 1205-1208. http://dx.doi.org/10.1109/icip.2012.6467082.
[24] Kalra, M. and Ghosh, D. (2012) Image Compression Using Wavelet Based Compressed Sensing and Vector Quantization.IEEE 11th International Conference on Signal Processing (ICSP), 1, 640-645.
[25] Kokareh, M., Biswas, P.K. and Chatterji, B.N. (2005) Texture Image Retrieval Using New Rotated Complex WaveletFilters. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35, 1168-1178.
[26] Balamurugan, V. and Anandha Kumar, P. (2008) An Integrated Color and Texture Feature Based Framework for ContentBased Image Retrieval Using 2D Wavelet Transform. IEEE International Conference on Computing, Communicationand Networking, 1-16. http://dx.doi.org/10.1109/icccnet.2008.4787734.
[27] Quellec, G., Lamard, M., Cazuguel, G., Cochener, B. and Roux, C. (2012) Fast Wavelet-Based Image Characterizationfor Highly Adaptive Image Retrieval. IEEE Transactions on Image Processing, 21, 1613-1623.
[28] Uma, R., Latha, and B.: Enhanced clustering of correlated probabilisticgraphs. Int. J. Sci. Eng. Res. (2015).
[29] Uma, R., Latha, B.: Multi agent based information retrieval.
[30] System for Multi-Modal database. Int. J. Print. Pack. Allied Sci.4 (4), 2286–2299 (2016).
[31] Uma, R., Latha, B.: Sub-topic modeling-a hierarchy model for topic correlations. Int. J. Control Theory Appl. 9(28), 175–179(2016).
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How to Cite
Uma, R., & Latha, B. (2018). An efficient voice based information retrieval using bag of words based indexing. International Journal of Engineering & Technology, 7(2.33), 622-627. https://doi.org/10.14419/ijet.v7i2.33.14850Received date: 2018-06-30
Accepted date: 2018-06-30
Published date: 2018-06-08