A hybridcontent based image retrieval system based onlocal binary pattern (LBP), color moment (CM) and edges

In this paper a hybrid content based image retrieval system (CBIR) based on local binary pattern (LBP), color moment (CM) and edges have been efficiently used for image retrieval. LBP is used as it is sensible to noise so the neighboring pixels comparison is easy. CM is used as it is efficient in indexing the images based on the color in image retrieval system. Edges can be used to efficiently discover the shape information. So it will be better to combine all the three features and utilized the variable property of all the three. Wang database have been used for the experimentation. All the 10 classes are used for the results comparison. The results suggested that the proposed hybrid method have the capability over individual methods and also over the LBP and CM method in combination in efficient image retrieval. Keyword-CBIR, Content Retrieval, LBP, CM, Edges

[1]  Toshikazu Kato,et al.  Database architecture for content-based image retrieval , 1992, Electronic Imaging.

[2]  Sandeep Kumar,et al.  A Review of Content Based Image Classification using Machine Learning Approach , 2012 .

[3]  T. Kaur,et al.  Novel Method for Edge Detection for Gray Scale Images using VC++ Environment , 2014 .

[4]  Arpita Mathur,et al.  Content Based Image Retrieval by Multi Features using Image Blocks , 2014 .

[5]  Wang Yong-sheng An algorithm for edge detection of gray-scale image based on mathematical morphology , 2005 .

[6]  Sonal Jain,et al.  A Survey on Breast Cancer Scenario and Prediction Strategy , 2014, FICTA.

[7]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  N. Puviarasan,et al.  Retrieval of Images Using Weighted Features , 2014 .

[10]  Katherine A. Heller,et al.  A Simple Bayesian Framework for Content-Based Image Retrieval , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Sonal Jain,et al.  Breast cancer statistics and prediction methodology: a systematic review and analysis. , 2015, Asian Pacific journal of cancer prevention : APJCP.

[12]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  A. M. Patil,et al.  Content Based Image Retrieval Using Color and Shape Features , 2012 .

[14]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[15]  James Ze Wang,et al.  Content-based image indexing and searching using Daubechies' wavelets , 1998, International Journal on Digital Libraries.

[16]  Mohammad Atique,et al.  Web Based Image Retrieval System Using Color, Texture and Shape Analysis: Comparative Analysis , 2013 .

[17]  Abbes Amira,et al.  Semantic content-based image retrieval: A comprehensive study , 2015, J. Vis. Commun. Image Represent..

[18]  Ashok Yadav,et al.  Image Classification by Combining Wavelet Transform and Neural Network , 2016 .

[19]  Shi Zhang,et al.  Independent component analysis based on adaptive artificial bee colony , 2016 .

[20]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[21]  Francesco Corea,et al.  Emotional speculative behavior in the option market , 2016 .

[22]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  P. Anandan,et al.  Curvelet based Image Compression using Support Vector Machine and Core Vector Machine – A Review , 2014 .

[24]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Sonal Jain,et al.  Analysis of k-means clustering approach on the breast cancer Wisconsin dataset , 2016, International Journal of Computer Assisted Radiology and Surgery.

[26]  Gadadhar Sahoo,et al.  Comparison of Content Based Image Retrieval Systems Using Wavelet and Curvelet Transform , 2012 .

[27]  Monika Jain,et al.  A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets , 2011 .

[28]  Ravi Mohan Sairam,et al.  Result Analysis on Content Base Image Retrieval using Combination of Color, Shape and Texture Features , 2013 .

[29]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[30]  Sonal Jain,et al.  Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis , 2016, Chinese journal of cancer.

[31]  Romain Murenzi,et al.  Fast texture database retrieval using extended fractal features , 1997, Electronic Imaging.

[32]  Ji Wan,et al.  Deep Learning for Content-Based Image Retrieval: A Comprehensive Study , 2014, ACM Multimedia.

[33]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[34]  S ViswaS Efficient Retrieval of Images for Search Engine by Visual Similarity and Re Ranking , 2013 .

[35]  Mahesh Prasanna,et al.  Image Processing Algorithms – A Comprehensive Study , 2014 .

[36]  Ying Liu,et al.  A survey of content-based image retrieval with high-level semantics , 2007, Pattern Recognit..

[37]  Karthik Ramani,et al.  Content-Based Image Retrieval Using Shape and Depth from an Engineering Database , 2007, ISVC.