Decision Fusion for Classification of Content Based Image Data

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• 关键词：Binarization ; Fusion ; Image classification ; Image retrieval ; Query classification ; t test ; Vector quantization
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10220
• 期：1
• 页码：121-138
• 参考文献：1.Bashir, M.B., et al.: Content-based information retrieval techniques based on grid computing: a review. IETE Techn. Rev. 30(3), 223–232 (2013)CrossRef
  2.Liao, B., Xu, J., Lv, J., Zhou, S.: An image retrieval method for binary images based on DBM and softmax classifier. IETE Techn. Rev. 32(4), 294–303 (2015)CrossRef
  3.Aouat, S., Larabi, S.: Outline shape retrieval using textual descriptors and geometric features. Int. J. Inf. Retr. Res. (IJIRR) 2(4), 60–81 (2012). doi:10.​4018/​ijirr.​2012100105
  4.Keyvanpour, M.R., Charkari, N.M.A.: Content based model for image categorization. In: 20th International Workshop on Database and Expert Systems Application, p. 4. IEEE (2009)
  5.Walia, E., Pal, A.: Fusion framework for effective color image retrieval. J. Vis. Commun. Image R. 25(6), 1335–1348 (2014). doi:10.​1016/​j.​jvcir.​2014.​05.​005 CrossRef
  6.Yıldız, O.T., Aslan, O., Alpaydın, E.: Multivariate Statistical Tests for Comparing Classification Algorithms. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 1–15. Springer, Heidelberg (2011)CrossRef
  7.Kekre, H.B., Thepade, S., Das, R.K.K., Ghosh, S.: Performance boost of block truncation coding based image classification using bit plane slicing. Int. J. Comput. Appl. 47(15), 45–48 (2012). ISSN: 0975-8887
  8.Thepade, S., Das, R., Ghosh, S.: Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding or content based image classification with discrete classifiers. In: Annual IEEE India Conference (INDICON), pp. 1–6 (2013). doi:10.​1109/​INDCON.​2013.​6726053
  9.Thepade, S.D., Das, R.K.K., Ghosh, S.: Image classification using advanced block truncation coding with ternary image maps. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds.) Advances in Computing, Communication, and Control. Communications in Computer and Information Science. Communications in Computer and Information Science, vol. 361, pp. 500–509. Springer, Heidelberg (2013)CrossRef
  10.Kekre, H.B., Thepade, S., Das, R., Ghosh, S.: Multilevel block truncation coding with diverse colour spaces for image classification. In: IEEE-International Conference on Advances in Technology and Engineering (ICATE 2013), pp. 1–7 (2013)
  11.Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979)CrossRef
  12.Shaikh, S.H., Maiti, A.K., Chaki, N.: A new image binarization method using iterative partitioning. Mach. Vis. Appl. 24(2), 337–350 (2013)CrossRef
  13.Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice Hall, Eaglewood Cliffs (1998)
  14.Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)CrossRef
  15.Bernsen, J.: Dynamic thresholding of gray level images. In: Proceedings of the International Conference on Pattern recognition (ICPR 1986), pp. 1251–1255 (1986)
  16.Liu, C.: A new finger vein feature extraction algorithm. In: IEEE 6th International Congress on Image and Signal Processing (CISP), vol. 1, pp. 395–399 (2013)
  17.Ramírez-Ortegón, M.A., Rojas, R.: Unsupervised evaluation methods based on local gray-intensity variances for binarization of historical documents. In: IEEE 20th International Conference on Pattern Recognition (ICPR), pp. 2029–2032 (2010)
  18.Yanli, Y., Zhenxing, Z.: A novel local threshold binarization method for QR image, In: IET International Conference on Automatic Control and Artificial Intelligence, pp. 224–227 (2012)
  19.Thepade, S., Das, R., Ghosh, S.: A novel feature extraction technique using binarization of bit planes for content based image classification. J. Eng. 13 (2014). doi:10.​1155/​2014/​439218 . Article ID 439218. Hindawi Publishing Corporation
  20.Kekre, H.B., Sarode, T.K., Raul, B.C.: Color image segmentation using Kekreʼs fast codebook generation algorithm based on energy ordering concept. In: Proceedings of the International Conference on Advances in Computing, Communication and Control, pp. 357–362 (2009)
  21.Lai, J.Z.C., Liaw, Y.C., Liu, J.: A fast VQ codebook generation algorithm using codeword displacement. Pattern Recogn. 41(1), 315–319 (2008)CrossRef MATH
  22.Liaw, Y.C., Lo, W., Lai, J.Z.C.: Image restoration of compressed image using classified vector quantization. Pattern Recogn. 35(2), 329–340 (2002)CrossRef MATH
  23.Nasrabadi, N.M., King, R.A.: Image coding using vector quantization: a review. IEEE Trans. Commun. 36(8), 957–971 (1998)
  24.Foster, J., Gray, R.M., Dunham, M.O.: Finite state vector quantization for waveform coding. IEEE Trans. Inf. Theory 31(3), 348–359 (1985)CrossRef
  25.Kim, T.: Side match and overlap match vector quantizers for images. IEEE Trans. Image Process. 1(2), 170–185 (1992). A Publication of the IEEE Signal Processing SocietyCrossRef
  26.Lai, J.Z.C., Liaw, Y.C., Lo, W.: Artifact reduction of JPEG coded images using mean-removed classified vector quantization. Signal Process. 82(10), 1375–1388 (2002)CrossRef MATH
  27.ElAlami, M.E.: A novel image retrieval model based on the most relevant features. Knowl. Based Syst. 24, 23–32 (2011)CrossRef
  28.Hiremath, P.S., Pujari, J.: Content based image retrieval using color, texture and shape features. In: 15th International Conference on Advanced Computing and Communications ADCOM, vol. 9, no. 2, pp. 780–784. IEEE (2007)
  29.Banerjee, M., Kundu, M.K., Maji, P.: Content-based image retrieval using visually significant point features. Fuzzy Sets Syst. 160(23), 3323–3341 (2009)MathSciNet CrossRef
  30.Jalab, H.A.: Image retrieval system based on color layout descriptor and Gabor filters. In: 2011 IEEE Conference on Open Systems, pp. 32–36. IEEE (2011)
  31.Shen, G.L., Wu, X.J.: Content based image retrieval by combining color texture and CENTRIST. In: IEEE International Workshop on Signal Processing, vol. 1, pp. 1–4 (2013)
  32.Irtaza, A. Jaffar, M.A. Aleisa, E., Choi, T.S.: Embedding neural networks for semantic association in content based image retrieval. Multimed. Tool Appl. 72(2), 1911–1931 (2014)
  33.Rahimi, M., Moghaddam, M.E.: A content based image retrieval system based on color ton distributed descriptors. Sig. Image Video Process. 9(3), 691–704 (2015)
  34.Subrahmanyam, M., Maheshwari, R.P., Balasubramanian, R.: Expert system design using wavelet and color vocabulary trees for image retrieval. Expert Syst. Appl. 39(5), 5104–5114 (2012)CrossRef
  35.Walia, E., Vesal, S., Pal, A.: An Effective and Fast Hybrid Framework for Color Image Retrieval, Sensing and Imaging. Springer, New York (2014)
  36.Sridhar, S.: Image Features Representation and Description Digital Image Processing, pp. 483–486. India Oxford University Press, New Delhi (2011)
  37.Dunham, M.H.: Data Mining Introductory and Advanced Topics, p. 127. Pearson Education, Upper Saddle River (2009)
  38.Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)CrossRef
  39.Thepade, S., Das, R., Ghosh, S.: Feature extraction with ordered mean values for content based image classification. Adv. Comput. Eng. (2014). doi:10.​1155/​2014/​454876 . Article ID 454876
  40.Liu, G.-H., Yang, J.-Y.: Content-based Image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)CrossRef
  41.Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)CrossRef
  42.Zhang, S., et al.: Query specific rank fusion for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 37(4), 803–815 (2015)MathSciNet CrossRef
  43.Bhattacharya, P., Gavrilova, M.: DT-RANSAC: a delaunay triangulation based scheme for improved RANSAC feature matching. In: Gavrilova, M.L., Tan, C.J.K., Kalantari, B. (eds.) Transactions on Computational Science XX. LNCS, vol. 8110, pp. 5–21. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-41905-8_​2 CrossRef
  
• 作者单位：Rik Das (15)
  Sudeep Thepade (16)
  Saurav Ghosh (17)
  
  15. Department of Information Technology, Xavier Institute of Social Service, Ranchi, Jharkhand, India
  16. Department of Information Technology, Pimpri Chinchwad College of Engineering, Pune, India
  17. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India
  
• 丛书名：Transactions on Computational Science XXIX
• ISBN：978-3-662-54563-8
• 卷排序：10220

文摘

Information recognition by means of content based image identification has emerged as a prospective alternative to recognize semantically analogous images from huge image repositories. Critical success factor for content based recognition process has been reliant on efficient feature vector extraction from images. The paper has introduced two novel techniques of feature extraction based on image binarization and Vector Quantization respectively. The techniques were implemented to extract feature vectors from three public datasets namely Wang dataset, Oliva and Torralba (OT-Scene) dataset and Corel dataset comprising of 14,488 images on the whole. The classification decisions with multi domain features were standardized with Z score normalization for fusion based identification approach. Average increase of 30.71% and 28.78% in precision were observed for classification and retrieval respectively when the proposed methodology was compared to state-of-the art techniques.