Abstract
Feature vector extraction has been the key component to define the success rate for content based image recognition. Block truncation coding is a simple technique which has facilitated various methods for effective feature vector extraction for content based image recognition. A new technique named Sorted Block Truncation Coding (SBTC) has been introduced in this work. Three different public datasets namely Wang Dataset, Oliva and Torralba (OT-Scene) Dataset and Caltech Dataset consisting of 6,221 images on the whole was considered for evaluation purpose. The technique has stimulated superior performance in image recognition when compared to classification and retrieval results with other existing techniques of feature extraction. The technique was also evaluated in lossy compression domain for the test images. Various parameters like precision, recall, misclassification rate and F1 score has been considered to evaluate the performances. Statistical evaluations have been carried out for all the comparisons by introducing paired t test to establish the significance of the findings. Classification and retrieval with proposed technique has shown a minimum of 14.4 % rise in precision results compared to the existing state-of-the art techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Keyvanpour, M.R., Charkari, N.M.: A content based model for image categorization. In: 20th International Workshop on Database and Expert Systems Application, pp. 1–4 (2009)
Mohanty, N., John, A.L.S., Manmatha, R., Rath, T.M.: Shape-based image classification and retrieval. Handb. Stat. 31, 249–267 (2013)
Niblack, W.: An Introduction to Digital Image Processing. Prentice Hall, Eaglewood Cliffs (1986)
Bernsen, J.: Dynamic thresholding of gray level images. In: ICPR 1986: Proceedings of the International Conference on Pattern Recognition, pp. 1251–1255 (1986)
Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)
Hiremath, P.S., Pujari, J.: Content based image retrieval using color, texture and shape features. In: 15th International Conference on Advanced Computing and Communications, vol. 9(2), pp. 780–784 (2007)
Gatos, B., Pratikakis, I., Perantonis, S.J.: Efficient binarization of historical and degraded document images. In: The Eighth IAPR Workshop on Document Analysis Systems, pp. 447–454 (2008)
Valizadeh, M., Armanfard, N., Komeili, M., Kabir E.: A novel hybrid algorithm for binarization of badly illuminated document images. In: 14th International CSI Computer Conference (CSICC), pp. 121–126 (2009)
Chang, Y.-F., Pai, Y.-T., Ruan, S.-J.: An efficient thresholding algorithm for degraded document images based on intelligent block detection. In: IEEE International Conference on Systems, Man and Cybernetics, SMC, pp. 667–672
Banerjee, M., Kundu, M.K., Maji, P.: Content-based image retrieval using visually significant point features. Fuzzy Sets Syst. 160(23), 3323–3341 (2009)
El Alami, M.E.: A novel image retrieval model based on the most relevant features. Knowl.-Based Syst. 24, 23–32 (2011)
Jalab, H.A.: Image retrieval system based on color layout descriptor and Gabor filters. In: IEEE Conference on Open System (ICOS), pp. 32–36 (2011)
Yue, J., Li, Z., Liu, L., Fu, Z.: Content-based image retrieval using color and texture fused features. Math. Comput. Model. 54(3–4), 1121–1127 (2011)
Kekre, H.B., Thepade, S., Das, R.K.K., Ghosh, S.: Image Classification using block truncation coding with assorted color spaces. Int. J. Comput. Appl. 44(6), 9–14 (2012). ISSN: 0975-8887
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
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)
Kekre, H.B., Thepade, S., Das, R.K.K., Ghosh, S.: Multilevel block truncation coding with diverse colour spaces for image classification. In: IEEE-International Conference on Advances in Technology and Engineering (ICATE), pp. 1–7 (2013)
Thepade, S., 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.) ICAC3 2013. CCIS, vol. 361, pp. 500–509. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36321-4_48
Thepade, S., Das, R.K.K., 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: India Conference (INDICON), pp. 1–6. IEEE (2013). doi:10.1109/INDCON.2013.6726053
Shaikh, S.H., Maiti, A.K., Chaki, N.: A new image binarization method using iterative partitioning. Mach. Vis. Appl. 24(2), 337–350 (2013)
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)
Irtaza, A., Jaffar, M.A., Aleisa, E., Choi, T.S.: Embedding neural networks for semantic association in content based image retrieval. Multimedia Tool Appl. 72, 1911–1931 (2013)
Rahimi, M., Moghaddam, M.E.: A content based image retrieval system based on Color ton Distributed descriptors. Sig. Image Video Process. 9, 691–704 (2013)
Thepade, S., Das, R.K.K., Ghosh, S.: A novel feature extraction technique using binarization of bit planes for content based image classification. J. Eng. 2014, Article ID 439218, 13 (2014). doi:10.1155/2014/439218, Hindawi Publishing Corporation
Chaki, N., Shaikh, S.H., Saeed, K. (eds.): Exploring Image Binarization Techniques. SCI, vol. 560. Springer, Heidelberg (2014)
Walia, E., Pal, A.: Fusion framework for effective color image retrieval. J. Vis. Commun. Image R. 25, 1335–1348 (2014)
Wang, J.W.J., Min, K., Jeung, Y.-C., Chong, J.-W.: Improved BTC using luminance bitmap for color image compression. In: 2nd International Congress on Image and Signal Processing, pp. 1–5. IEEE (2009). doi:10.1109/CISP.2009.5304208
Chou, Y.-C., Chang, H.-H.: A high payload data hiding scheme for color image based on BTC compression technique. In: IEEE Fourth International Conference on Genetic and Evolutionary Computing ICGEC, pp. 626–629 (2010)
Sridhar, S.: Digital Image Processing. Oxford University Press, Oxford (2011)
Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. Consum. Electron. 38(1), 18–34 (1992)
Yang, A.Y., Wright, J., Ma, Y., Sastry, S.S.: Unsupervised segmentation of natural images via lossy data compression. Comput. Vis. Image Underst. 110(2), 212–225 (2008)
Han, J., Kamber, M.: Data mining: concepts and techniques. The Morgan Kaufmann Series in Data Management Systems, pp. 89–90 (2001)
Cunningham, P., Delany, S.J.: k-Nearest neighbour classifiers. Multiple Classifier Syst. 34, 1–17 (2007)
Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007)
Dunham, M.H.: Data Mining Introductory and Advanced Topics, p. 127. Pearson Education, Upper Saddle River (2009)
Yıldız, O.T., Aslan, Ö., Alpaydın, E.: Multivariate statistical tests for comparing classification algorithms. In: Coello, C.A. (ed.) LION 2011. LNCS, vol. 6683, pp. 1–15. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Thepade, S., Das, R., Ghosh, S. (2015). Novel Technique in Block Truncation Coding Based Feature Extraction for Content Based Image Identification. In: Gavrilova, M., Tan, C., Saeed, K., Chaki, N., Shaikh, S. (eds) Transactions on Computational Science XXV. Lecture Notes in Computer Science(), vol 9030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47074-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-47074-9_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47073-2
Online ISBN: 978-3-662-47074-9
eBook Packages: Computer ScienceComputer Science (R0)