International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 7, No. 10, 8 Sep. 2015
Cover page and Table of Contents: PDF (size: 662KB)
Content Based Image Recognition by Information Fusion with Multiview Features
Full Text (PDF, 662KB), PP.61-73
Views: 0 Downloads: 0
Author(s)
Rik Das
1,*
Sudeep Thepade
2
Saurav Ghosh
3
Index Terms
Local Threshold, Partial DCT coefficient, KNN Classifier, Fusion based Recognition, t test
Abstract
Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.
Cite This Paper
Rik Das, Sudeep Thepade, Saurav Ghosh, "Content Based Image Recognition by Information Fusion with Multiview Features", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.10, pp.61-73, 2015. DOI:10.5815/ijitcs.2015.10.08
Reference
[1]Lee, Y., Kim, B., Rhee, S.: Content-based Image Retrieval using Spatial-color and Gabor Texture on a Mobile Device. Computer Science and Information Systems, Vol. 10, No. 2, 807-823. (2013).
[2]Chandrashekar, G. & Sahin, F.: A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. (2014).
[3]Chen, Y. et al.: Otsu?s thresholding method based on gray level-gradient two-dimensional histogram. 2nd International Asia Conference on Informatics in Control, Automation and Robotics, 282-285 (2010).
[4]Farid, S. & Ahmed, F.: Application of Niblack?s method on images. In 2009 International Conference on Emerging Technologies, ICET 2009. pp. 280-286. (2009).
[5]Goto, H.: Redefining the DCT-based feature for scene text detection. International Journal on Document Analysis and Recognition, 11(1), p.1-8. (2008).
[6]Sisodia, D., Singh, L. & Sisodia, S.: Incremental learning algorithm for face recognition using DCT. IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN). IEEE, 282-286. (2013).
[7]Kekre, H.B., Thepade, S., Banura, V.K. & Bhatia, A.: Image Retrieval using Fractional Coefficients of Orthogonal Wavelet Transformed Images with Seven Image Transforms. International Journal of Computer Applications (0975 – 8887). Vol 30(1), 14-20. (2011). 8.
[8]Chang, R., Lin, S., Ho, J., Fann, C., Wang, Y.: A Novel Content Based Image Retrieval System using K-means/KNN with Feature Extraction. Computer Science and Information Systems, Vol. 9, No. 4, 1645-1662. (2012).
[9]Wang, X.Y., Yu, Y.J. & Yang, H.Y.: An effective image retrieval scheme using color, texture and shape features. In Computer Standards and Interfaces. 59-68. (2011).
[10]Park, D.K., Jeon, Y.S. & Won, C.S.: Efficient use of local edge histogram descriptor. In MULTIMEDIA ’00 Proceedings of the 2000 ACM workshops on Multimedia. 51-54.(2000).
[11]Kim, W.Y. & Kim, Y.S.: Region-based shape descriptor using Zernike moments. Signal Processing: Image Communication, 16(1), 95-102. (2000).
[12]Kekre HB, Thepade S, Das RKK & Ghosh S.: Perfor-mance Boost of Block Truncation Coding based Image Classification using Bit Plane Slic-ing. International Journal of Computer Applications 47(15): 45-48, (ISSN:0975-8887) (2012).
[13]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: India Conference (INDICON), IEEE Digital Object Identifier: 10.1109/INDCON.2013.6726053, 1 – 6 (2013).
[14]Thepade S, Das RKK & Ghosh S: Image classification using advanced block truncation coding with ternary image maps. Advances in Computing, Communication and Control, Volume 361, DOI: 10.1007/978-3-642-36321-4_48, pp.500-509: Springer Berlin Heidelberg (2013).
[15]Kekre H.B., Thepade S, Das R & Ghosh S.: Multilevel Block Truncation Coding With Di-verse Colour Spaces For Image Classification. IEEE-International conference on Advances in Technology and Engineering (ICATE), 1-7 (2013).
[16]Otsu,N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, 62-66. (1979).
[17]Shaikh, S. H., Maiti, A. K., & Chaki, N.: A new image binarization method using iterative partitioning. Machine Vision and Applications, 24(2), 337-350 (2013).
[18]Niblack,W.: An Introduction to Digital Image Processing: Prentice Hall, Eaglewood Cliffs (1986).
[19]Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000).
[20]Bernsen, J.: Dynamic thresholding of gray level images.In: ICPR’86: Proceedings of the International Conference on Pattern Recognition, pp. 1251–1255 (1986).
[21]Liu.C.:A new finger vein feature extraction algorithm. IEEE 6th. International Congress on Image and Signal Processing (CISP),: 1, 395-399. (2013)
[22]Rami?rez-Ortego?n, M.A. & Rojas R.: Unsupervised Evaluation Methods Based on Local Gray-Intensity Variances for Binarization of Historical Documents, IEEE 20t. International Conference on Pattern Recognition (ICPR); 2029- 2032. (2010).
[23]Yanli, Y. & Zhenxing, Z.: A novel local threshold binarization method for QR image, IET International Conference on Automatic Control and Artificial Intelligence; 224-227. (2012).
[24]Thepade,S., Das, R. & Ghosh, S.: A Novel Feature Extraction Technique Using Bi-narization of Bit Planes for Content Based Image Classification. Journal of Engineering, vol. 2014, Article ID 439218, 13 pages. doi:10.1155/2014/439218 Hindawi Publishing Corporation (2014).
[25]Kekre, H.B., Thepade, S., Viswanathan, A., Varun, A., Dhwoj, P. & Kamat, N.: Palm print identification using fractional coefficients of Sine/Walsh/Slant transformed palm print images. Communications in Computer and Information Science. 214-220. (2011).
[26]Thepade, S., Das, R., & Ghosh, S.:Feature Extraction with Ordered Mean Values for Content Based Image Classification. Advances in Computer Engineering, vol. 2014, Article ID 454876, 15 pages. (2014) doi:10.1155/2014/454876. (2014).
[27]El Alami, M.E.: A novel image retrieval model based on the most relevant features. Knowl.-Based Syst. 24, 23–32. (2011).
[28]Hiremath, P. S., & Pujari, J.: Content Based Image Retrieval Using Color, Texture and Shape Features. In: 15th International Conference on Advanced Computing and Communi-cations , 9(2), 780-784. (2007).
[29]Banerjee, M., Kundu, M. K., & Maji, P.: Content-based image retrieval using visually significant point features. Fuzzy Sets and Systems, 160(23), 3323-3341 (2009).
[30]Jalab, H.A.: Image retrieval system based on color layout descriptor and Gabor filters. In: IEEE Conf. Open Syst. (ICOS) 32-36 (2011).
[31]Shen, G.L. & Wu, X.J.: Content based image retrieval by combining color texture and CENTRIST, IEEE internationalworkshoponsignal processing, vol. 1, 1–4 (2013).
[32]Irtaza, A. Jaffar, M.A. Aleisa, E., Choi, T.S.: Embedding neural networks for semantic as-sociation in content based image retrieval. Multimedia Tool Appl. 1–21 (2013).
[33]Rahimi, M., Moghaddam, M.E.: A content based image retrieval system based on Color ton Distributed descriptors. Signal image and video processing, 1–14 (2013).
[34]Subrahmanyam, M., Maheshwari, R.P. & Balasubramanian, R. : Expert system design using wavelet and color vocabulary trees for image retrieval. Expert Systems with Applications, 39(5).5104-5114 (2012).
[35]Walia, E., Vesal, S. & Pal. A.: An Effective and Fast Hybrid Framework for ColorImage Retrieval, Sensing and Imaging. DOI: 10.1007/s11220-014-0093-9, Springer US (2014).
[36]Sridhar.,S. : Digital Image Processing : Oxford University Press (2011).
[37]Dunham, M.H.: Data Mining Introductory and Advanced Topics: Pearson Education, p. 127 (2009).
[38]Y?ld?z O.T., Aslan, and Alpayd?n E,: Multivariate Statistical Tests for Comparing Classi-fication Algorithms : Lecture Notes in Computer Science Volume 6683, 1-15, Springer Berlin Heidelberg (2011).
[39]Liu, G-H. & Yang, J-Y.: Content-Based Image retrieval using color difference histogram, Pattern Recognition, 46(1) 188-198 (2013).
[40]Arai,K.: Method for Object Motion Characteristic Estimation Based on Wavelet Multi-Resolution Analysis: MRA, IJITCS, vol.6, no.1, pp.41-49, 2014. DOI: 10.5815/ijitcs.2014.01.05.
[41]Barde, S., Zadgaonkar, A S, Sinha, G R: PCA based Multimodal Biometrics using Ear and Face Modalities, IJITCS, vol.6, no.5, pp.43-49, 2014. DOI: 10.5815/ijitcs.2014.05.06.
[42]Das, R. and Bhattacharya, S., A Novel Feature Extraction Technique for Content Based Image Classification in Digital Marketing Platform, American Journal Of Advanced Computing, Vol. 2(1), 2015, pp. 17-24.
[43]Thepade, S., Das, R. & Ghosh, S.: Novel Technique in Block Truncation Coding Based Feature Extraction for Content Based Image Identification, Transactions on Computational Science XXV, Springer Berlin Heidelberg, 2015, pp. 55-76.
[44]Thepade, S., Das, R. & Ghosh, S.: A Novel Feature Extraction Technique with Binarization of Significant Bit Information, International Journal of Imaging and Robotics?, Vol. 15 (3), 2015, pp. 164-178.
[45]Thepade, S., Das, R. & Ghosh, S.: Content Based Image Classification with Thepade's Static and Dynamic Ternary Block Truncation Coding, Vol.4 (1), 2015, pp. 13-17.