Content Based Image Retrieval using Improved Local Tetra Pattern and Neural Network
Abstract
Because of the exponential increase in digital images, image databases have grown to a much large volume that retrieval of required images from these databases is a very difficult task. Image retrieval can also be practiced via human annotation but it cannot be trusted. So, now a days, a more relied and effective method to retrieve relevant images from a large databse is Content Based Image Retrieval (CBIR). These days, main focus is to achieve a more accurate CBIR algorithm so that retrieval efficiency can be increased. In this paper, we have proposed a CBIR algorithm which retrieves images from database with increased precision. For feature extraction, we have used Improved Local Tetra Patterns which is a texture descriptor and based on the direction of pixel. Direction of all the pixels in an image is calculated; based on direction of each pixel, an 8-bit pattern is achieved which is further divided into 7 binary patterns and 1 magnitude pattern. For experimental purposes, we have used Corel database which has been used by most researchers. After performing experiment on the said dataset, improved precision rates were observed when proposed technique was compared with some previous approaches. Average precision observed in our experiment was 82%.
References
B. S. Manjunath, J. R. Ohm, V. V. Vasudevan and A. Yamada, “Color and texture descriptors”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 703-715, 2001.
M. Lamard, G. Cazuguel, G. Quellec, L. Bekri, C. Roux, and B. Cochener, “Content based image retrieval based on wavelet transform coefficients distribution”, IEEE 29th Annual International Conference on Engineering in Medicine and Biology Society (EMBS), pp. 4532-4535, 22-26 August 2007, Lyon, France, 2007.
I. J. Sumana, G. Lu, and D. Zhang, “Comparison of curvelet and wavelet texture features for content based image retrieval”, IEEE International Conference on Multimedia and Expo (ICME), pp. 290-295, 9-13 July 2012, Melbourne, Australia, 2012.
J. R. Smith and S. F. Chang, “Transform features for texture classification and discrimination in large image databases”, IEEE International Conference on Image Processing, vol. 3, pp. 407-411, 13-16 November 1994, Austin, USA, 1994.
D. Zhang, A. Wong, M. Indrawan, and G. Lu, “Content-based image retrieval using Gabor texture features”, 1st IEEE Pacific-Rim Conference on Multimedia, pp. 392-395, 13-15 December, Sydney, Australia, 2000.
H. Tamura, S. Mori, and T. Yamawaki, “Textural features corresponding to visual perception”, IEEE Trans. on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460-473, 1978.
T. Deselaers, D. Keysers, and H. Ney, “Features for image retrieval: an experimental comparison”, Information Retrieval, vol. 11, no. 2, pp. 77-107, 2008.
M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele,
and P. Yanker, “Query by image and video content: The QBIC system”, Computer, vol. 28, no. 9, pp. 23-32, 1995.
A. P. Pentland, “Fractal-based description of natural scenes”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 661-674, 1984.
L. M. Kaplan, “Extended fractal analysis for texture classification and segmentation”, IEEE Trans. on Image Processing, vol. 8, no. 11, pp. 1572-1585, (1999).
H Yu, M Li, H J Zhang and J Feng, “Color texture moments for content-based image retrieval”, IEEE International Conference on Image processing, vol. 3, pp. 929-932, 22-25, 2002, New York, USA, 2002.
I. J Sumana, M. M. Islam, D. Zhang and L. Guojun, “Content based image retrieval using curvelet transform”, 10th IEEE Workshop on Multimedia Signal Processing, pp. 11-16, 8-10 October 2008, Queensland, Australia, 2008.
M. Singha, K. Hemachandran and A. Paul, “Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram”, IET Image Processing, vol. 6, no. 9, pp.1221-1226, 2012.
G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors”, ACM 4th international conference on Multimedia, pp. 65-73, 18 - 22 November 1996, Boston, USA, 1996.
C. H. Su, H. S. Chiu and T. M. Hsieh, “An efficient image retrieval based on HSV color space”, IEEE International Conference on Electrical and Control Engineering (ICECE), pp. 5746-5749, 16-18 September 2011, Yichang, China, 2011.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions”, IEEE Trans. on Image Processing, vol. 19, no. 6, pp. 1635–1650, 2010.
B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local derivative pattern versus local binary pattern: Face recognition with higher-order local pattern descriptor”, IEEE Trans. on Image Processing, vol. 19, no. 2, pp. 533–544, 2010.
S. Murala, R. P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval”, IEEE Trans. on Image Processing, vol. 21, no. 5, pp. 2874-2886, 2012.
M. E. El Alami, “New matching strategy for content based image retrieval system”, Applied Soft Computing, vol. 14, pp. 407-418, 2014.
E. Yildizer, A. M. Balci, M. Hassan and R. Alhajj, “Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble”, Expert Systems with Applications, vol. 39, no. 3 pp. 2385-2396, 2012.
S. M. Youssef, “ICTEDCT-CBIR Integrating Curvelet Transform with enhanced dominant colors extraction and texture analysis for Efficient Content based Image Retrieval”, Computers & Electrical Engineering, vol. 38, no. 5, pp. 1358-1376, 2012.
