Performance Analysis of Fuzzy C-Means Clustering using Multichannel Decoded Local Binary Pattern
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : V. Alan Gowri Phivin, A.C. Subhajini
|DOI : 10.14445/22315381/IJETT-V61P209|
MLA Style: V. Alan Gowri Phivin, A.C. Subhajini"Performance Analysis of Fuzzy C-Means Clustering using Multichannel Decoded Local Binary Pattern" International Journal of Engineering Trends and Technology 61.1 (2018): 49-55.
APA Style:V. Alan Gowri Phivin, A.C. Subhajini, (2018). Performance Analysis of Fuzzy C-Means Clustering using Multichannel Decoded Local Binary PatternInternational Journal of Engineering Trends and Technology, 61(1), 49-55.
The construction of large database with thousands of data storage and image acquisitions have been facilitated with developments. Suitable information system requires proper handling of these datasets in efficient manner. Content-Based Image Retrieval (CBIR) is commonly used system to handle these datasets and on the basis of image substance the images that are related to the user given query for which the CBIR extracts the images from large image databases. The goal of feature extraction is to obtain the most relevant information from the original data and represent that information in a lower dimensionality space. Local Binary Pattern (LBP) based descriptors have been used for the purpose of image feature description. Local binary pattern has widely increased the popularity due to its simplicity and effectiveness in several applications. We used adder- decoder based two schemas for the mixture of the LBPs from over one channel. Finally, Calculate feature vector to form a single feature vector. Clustering the image using Fuzzy C-means clustering under semi-supervised framework.The experiments square measure executed over six benchmark color texture image databases. The performance of the proposed descriptors improved for three input channels and also in the RGB color space. The performance of mdLBP is also superior to non-LBP descriptors. It is pointed out that mdLBP outperforms the state-of-the-art descriptors over large databases.
 Metty Mustikasari, Sarifuddin Madenda, “Content Based Image Retrieval Using Local Color Histogram”, International Journal of Engineering Research ,Volume No.3, Issue No.8, pp : 507-511, 2014.
 G.H. Liu and J.Y. Yang, “Content-based image retrieval using color histogram,” Pattern Recognition, vol. 46, no. 1, pp. 188-198, 2013.
 Lining Zhang, Lipo Wang And Weisi Lin, “Generalized Biased Discriminant Analysis For Content-Based Image Retrieval”-2012
 Chuen-Horng Lin And Rong-Tai Chen, “A Smart Content-Based Image Retrieval System Based On Color And Texture Feature”-2008
 S.Liao, M.W.K. Law, and A.C.S. Chung, “Dominant local binary patterns for texture classification,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107-1118, 2009.
 Hassan Farsi, Sajad Mohamadzadeh ,Colour and texture feature-based image retrieval by using Hadamard matrix in discrete wavelet transform 2013.
 Yan Liang, Le Dong, Shanshan Xie, Na Lv and Zongyi Xu, “Compact feature based clustering for large-scale image retrieval, 2014
 Chuen-Horng Lin and Rong-Tai Chen, “A smart content-based image retrieval system based on color and texture feature, 2008.
 P.S. Hiremath and Jagadeesh Pujari, “Content Based Image Retrieval using Color, Texture and Shape features. “,2017.
 Dengsheng Zheng, Aylwin Wong, Maria Indrawan, Guojan Lu, “Content based Image Retrieval Using Gabor Texture Feature “, 2001.
 Guang-Hai Liu, Jing-Yu Yang, ZuoYong Li, “Content-based image retrieval using computational visual attention model, ”2014
 T.W.Chiang and T.-W. Tsai, “Content-based image retrieval using multiresolution color and texture feature”, 2016
 Ali Montazer, Davar Giveki, “Content based image retrieval system using clustered scale invariant feature transforms” , 2015
V. Alan Gowri Phivin, A.C. Subhajini