Complementary feature extraction approach in CBIR

Since last few years, Content Base Image Retrieval (CBIR) system has got more attention from its generic to specific use. CBIR depends upon visual low-level feature extraction i-e color, texture, shape and spatial layout. In this paper, a Local Binary Patterns (LBP) has been employed for texture analysis of image and also it is compared with average RGB color image descriptor method. And then a complementary feature extraction approach using average RGB color and LBP texture method has been proposed for CBIR. Euclidean distance is used as similarity measure for finding similar images in the database. The experimental results are generated using MATLAB. The obtained results proved that the accuracy and efficiency of proposed method in terms of overall precision, recall, f_measure and retrieval time are quite higher than single color and texture feature extraction approach.

[1]  B.L. Deekshatulu,et al.  Learning Semantics in Content Based Image Retrieval (CBIR) - A Brief Review , 2010, 2010 Second Vaagdevi International Conference on Information Technology for Real World Problems.

[2]  Muhammad Ikram,et al.  Image Retrieval in Multimedia Databases: A Survey , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[3]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[4]  Muhammad Jawad Hussain,et al.  Complementary semantic model for content-based image retrieval , 2014, 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP).

[5]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[6]  Yihong Gong,et al.  An image database system with content capturing and fast image indexing abilities , 1994, 1994 Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[7]  Ashutosh Gupta,et al.  Image retrieval based on color, shape and texture , 2015, 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

[8]  Swati Goel,et al.  A Survey on Recent Image Indexing and Retrieval Techniques for Low-Level Feature Extraction in CBIR Systems , 2015, 2015 IEEE International Conference on Computational Intelligence & Communication Technology.

[9]  Xiongfei Li,et al.  Multimodal Image Retrieval Based on Annotation Keywords and Visual Content , 2009, 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009).

[10]  R. H. Goudar,et al.  An integrated approach to Content Based Image Retrieval , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).