The week's pick
Reseach Article
Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 19 |
| Year of Publication: 2018 |
| Authors: Poonam Rani, Sonika Jindal |
10.5120/ijca2018916327
|
Poonam Rani, Sonika Jindal . Features Extraction using Local Binary Patterns and Steerable Pyramids for Efficient Image Retrieval. International Journal of Computer Applications. 179, 19 ( Feb 2018), 25-30. DOI=10.5120/ijca2018916327
Abstract
This present a hybrid approach of image classification using KNN and feature extraction using LBP and steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image. In this k-nearest neighbor image classification mechanism is used to fetch the appropriate images from the database image set using the query image and the database images are reduced to images returned after classification mechanism which leads to decrease in the number of irrelevant images. Steerable pyramid applied to extract features from query image and candidate images retrieved from the KNN and store them in feature features. Local Binary Pattern (LBP) is one of the techniques used in image classification and has been used for extracting the shape of the images. The experimental evaluation of the system is based on a Wang data set. Various parameters like precision, recall, computation time and matching time have been computed to analyze the results that are recorded iteratively for different images as input. From the experimental results, it is evident that proposed system performs significantly better and faster compared with other existing systems. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them giving better retrieval results for almost all different classes of images in the data set.
References
- M. Fakheri, T. Sedghi, M. . G. Shayesteh1 and M. C. Amirani, "Groundwork for image retrieval using machine learning and statistical similarity matching techniques," IET Image Process, pp. 1-11, 2013.
- P. MANIPOONCHELVI and K. MUNEESWARAN, "Multi region based image retrieval system," Indian Academy of Sciences, pp. 333-344, 2014.
- Savita Gandhani1, Rakesh Bhujade1 and Amit Sinhal1,”An improved and efficent implementation of cbir system based on combined features” IET,pp.353-359,2013.
- Y. Chen, J. . Z. Wang and R. Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE, pp. 1187-1201, 2005.
- R. Fergus, L. Fei-Fei, P. Perona and A. Zisserman, "Learning Object Categories from Google’s Image Search," IEEE, 2005.
- Y. Chen, X. Li, A. Dick and A. v. d. Hengel, "Boosting Object Retrieval With Group Queries," IEEE, pp. 765-768, 2012.
- A.Khokher,R.Talwar”Content based image retrieval feature extraction and applications,”IJCA,pp 9-14,2012.
- Savita Gandhani1, Rakesh Bhujade1 and Amit Sinhal1,”An improved and efficent implementation of cbir system based on combined features” IET,pp.353-359,2013
- D.jeyabharathi and A.Suruliandil”Performance analysis of feature extraction and classification tech.,”ICCPCT”, pp1211-1214,2013.
- H. Xie, Y. Ji and Y. Lu, "An Analogy-Relevance Feedback CBIR Method Using Multiple Features," IEEE, pp. 83-86, 2013.
- C.-H. Lin, R.-T. Chen and Y.-K. Chan, "A content-based image retrieval based on color and texture feature," ELSEVIER, p. 658–665, 2009.
- Y. Chen and J. Z. Wang, "A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval," IEEE, pp. 1252-1267, 2002.
- K. BELATTAR and S. MOSTEFAI , "CBIR with RF:which Technique for which Image," IEEE conference 2013.
- Y. Chen, J. . Z. Wang and R. Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE, pp. 1187-1201, 2005.
- Savvas A Chatzichristofis, Yiannis S Boutalis,”color and edge directivity descriptor”, springer, pp.312-322, 2008.
- Chuen-horng lin “Image Retrieval Using Modified Color Variation Co-occurrence Matrix”,IAE,pp.42-51,2008.
- Adrijit Basu”Shape Based Image Representation and Retrieval”,IJERMT,2015,pp.81-86.
- Sreedevi S. and Shinto Sebastian, "Fast Image Retrieval with Feature Levels," IEEE, 2013.
- ] S. Kumar, S. Jain and T. Zaveri, "Parallel Approach To Expedite Morphological Feature Extraction Of Remote Sensing Images For Cbir System," IEEE, pp. 2471-2474, 2014.
- K. Juneja, A. Verma , S. Goel and S. Goel , "A Survey on Recent Image Indexing and Retrieval Techniques for Low-level Feature Extraction in CBIR systems," IEEE, pp. 67-72, 2015.
- S. Ezekiel, Mark G. Alford, David Ferris and Eric Jones,, "Multi-Scale Decomposition of Content Based ge Retrieval," IEEE, 2013.
- J. Z. Wang, J. Li and G. Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries," IEEE, pp. 947-963, 2001.
- S. . A. Chatzichristofis and Y. S. Boutalis, "CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval," Springer-Verlag Berlin Heidelberg, pp. 313-322, 2008.
- M.Perdoch “Efficient representation of local geometry for large scale object retrieval”IEEE conference,2009.
- H.jegou,”Aggregating local descriptors into a compact image representation”,IEEE conference,2010.
- Relja Arandjelovi”Three things everyone should know to improve object retrieval”,IEEE conference,2010.
- B. Kaur and S. Jindal, "An implementation of Feature Extraction over medical images on OPEN CV Environment".
- S. M. H. Khan, A. Hussain and I. F. T. Alshaikhl, "Comparative study on Content-Based Image Retrieval (CBIR)," IEEE, pp. 61-66, 2013.
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