Journal Title : International Journal of Modern Trends in Engineering and Science
Author’s Name : K Nagarajan
Volume 02 Issue 08 Year 2015
ISSN no: 2348-3121
Page no: 3-6
Abstract— Content Based Image Retrieval (CBIR) is a very important research area in the field of image processing, and comprises of low level feature extraction such as color, texture and shape and similarity measures for the comparison of images. This information uses to search and analysis required image from outsized image database based on the user’s requests in the structure of a query image. Texture based CBIR is a fast growing research area in recent years. In this paper we analyze the performance of feature extraction techniques Perceptual feature and Tetra Pattern using nearest neighbours classification techniques. Experimental results are measured by F-Score and Recognition Rate matrices. Experiment results show that Tetra pattern technique gives better retrieval result then Perceptual Features Techniques.
Keywords— CBIR, Tetra Pattern, Perceptual Feature, Nearest neighbours, Rocognition rate, F-score
 Noureddine Abbadeni “Computational Perceptual Features for Texture Representation and Retrieval,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 1, JANUARY (2011).
 Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval Subrahmanyam Murala, R. P. Maheshwari, Member, IEEE, and R. Balasubramanian, Member, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 5, MAY (2012).
 “Content based image retrieval techniques – Issues, analysis and the state of the art” by Darshak G. Thakore, A. I. Trivedi.
 Ahonen, T., Hadid, A., PietikaKinen, M.: „Face description with local binary patterns: application to face recognition‟, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 2037–2041
 Chellappa, R. and Chatterjee, S. (1985), `Classification of textures using Gaussian Markov random fields’, Acoustics, Speech, and Signal Processing, IEEE Transactions on 33(4), 959-963.
 C.-H. Lin, C.-W. Liu, H.-Y. Chen, “Image retrieval and classification using adaptive local binary patterns based on texture features,” Published in IET Image Processing Received on 14th September (2011) Revised on 7th July (2012).
 T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a, “Multiresolution grayscale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971–987, Jul (2002).
 T. Ojala, M. Pietikainen, and T. Maenpaa “Gray scale and rotation invariant texture classification with local binary patterns,” Computer Vision-ECCV 2000, pp. 404- 420, (2000).
 T. Maenpa , T. Ojala, M. Pietikainen, and M. Soriano, “Robust texture classification by subsets of Local Binary Patterns,” in Proc. 15th International Conference on Pattern Recognition. pp. 947-950, (2000).