Content Based Image Retrieval (CBIR) plays a significant role in case of image processing. Generally, in case of large scale dataset the two problems which are common viz. lower memory cost and higher retrieval accuracy. To solve the problem of the large scale retrieval the mixture of subspaces image representation is used. In this approach the group of the local descriptors of every individual image is used for global image representation. The Principal Component Analysis (PCA) is used for the dimensionality reduction. So that large number of images can be retrieved easily. Accuracy of the proposed system is measured in terms of mean average precision. Through the experiment it shows that the proposed system gives better result than earlier system.

1. Chee Sun Won, Dong Kwon Park, and Soo-Jun Park , "Efficient Use of MPEG-7 Edge Histogram Descriptor", ETRI Journal, volume 24, Number 1, Feburary 2002.
2. F. Perronnin and C. Dance , "Fisher kernels on visual vocabularies for image categorization", in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2007, pp. 18.
3. F. Perronnin, Y. Liu, J. Sanchez, and H. Poirier , "Large Scale Image Retrieval with Compressed Fisher Vectors", Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010.
4. F. Perronnin, J. Sanchezz, and T. Mensink , "Improving the Fisher kernel for largescale image classification", in Proc. Eur. Conf. Comput. Vis. , 2010, pp. 143156.
5. H. Jegou, M. Douze, and C. Schmid, "Improving bag-of features for large scale image search,Int. J. Comput. Vis. , vol. 87, no. 3, pp. 316336, 2010.
6. H. Jegou, M. Douze, C. Schmid, and P. Perez, "Aggregating Local Descriptors into a Compact Image Representation ",Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010.
7. T. Tuytelaars, M. Fritz, K. Saenko, and T. Darrell, "The nbnn kernel",in Proc. IEEE Int. Conf. Comput. Vis. , 2011, pp. 824-1831.
8. M. Jain, R. Benmokhtar, P. Gros, and H. Jegou, Hamming embedding similaritybased image classification", in Proc. 2nd ACM Int. Conf. Multimedia Retrieval, 2012, article 19, DOI:10. 1145/2324796. 2324820.
9. M. Young, The Technical Writers Handbook. Mill Valley, CA: University Science, 1989.
10. H. Jegou, F. Perronnin, M. Douze, J. Sanchez, P. Perez, and C. Schmid, "Aggregating local image descriptors into compact codes", IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 9,pp. 1704-1716, Sep. 2012.
11. Jie Linyz, Ling-Yu Duany, Tiejun Huangy, Wen Gaoy, "Robust Fisher Codes for Large Scale Image Retrieval", IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), May 2013.
12. T. Takahashi, T. Kurita, "Mixture of Subspaces Image Representation and Compact Coding for Large Scale Image", in IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 37, No. 7, July 2015.
13. Thomas Deselaers and Daniel Keysers and Hermann Ney,"Features for Image Retrieval: An Experimental", Journal Information Retrieval archive, Volume 11 Issue 2, April 2008 Pages 77 - 107 .
14. Qilong Wang, Peihua Li, Lei Zhang, Wangmeng Zuo, "Towards Effective Codebookless Model for Image Classification", Computer Vision and Pattern Recognition, July 2015.
15. Chao Chen, Coral Gables, Mei-Ling Shyu, Shu-Ching Chen , "Gaussian Mixture Model-Based Subspace Modeling for Semantic Concept Retrieval", Information Reuse and Integration (IRI), 2015 IEEE International Conference,13-15 Aug. 2015.
16. Jadhav Shweta D and Prof. Shahane Nitin M. ," A Review on Content Based Image Retrieval using Mixture Model for Image Representation ", IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613.
17. Jadhav Shweta D and Prof. Shahane Nitin M, "Content Based Image Retrieval using Mixture of Subspaces Image Representation",cPGCON-2016.

Content Based Image Retrieval (cbir) Image Retrieval Subspaces.