Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 186-190

Figures :06

Tables : --

Publication Link : Content Based Effective Searching of Images from Large Data Repository

 

 

 

Shine Raj G : is working as Asst. Professor in Computer Science department. He has more than 8 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, and image mining

Anver Muhammed K.M : is working as System Administrator in Computer Science department. He has more than 10 years experience in System Administration and Implementation. His research interests include image processing and network security.

 

 

 

 

 

 

 

CBIR

Feature Extraction

Bag of Words

Searching an image from a large image data base takes lot of time. Searching data based on features will gain tremendous amount of gain in time because very few features are stored for each image. Like wise bag of words also store lesser information of each image and hence very effective in searching of image .

 

 

 

 

 

 

 

 

 

[1] Long, F. H., H. J. Zhang and D. D. Feng, Fundamentals of content-based image retrieval, in Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, Springer-Verlag, pp.1- 26, New York, 2003. [2] Lowe, D. G., Distinctive image features from scale invariant features, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. [3] Kumar, A. and Sminchisescu, C. Support kernel machines for object recognition, in ICCV, 2007. [4] Zhang, H., Berg, A., Maire, M. and Malik, J., Svm-knn: Discriminative nearest neighbor classification for visual category recognition, in CVPR, 2006. [5] Yang, C. C., Prasher, S. O. , Enright, P., Madramootoo, C., Burgess, C., Goel, P. K., Callum, I., Application of decision tree technology for image classification using remote sensing data, in Agricultural Systems 76, pp. 1101-1117, 2003. [6] K. Barnard, P. Duygulu, D. Forsyth. Modeling the Statistics of Image Features and Associated Text. Proc. Document Recognition and Retrieval, Electronic Imaging, San Jose, CA, Jan. 2002. [7] Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D., & Jordan, M. (2003).Matching words and pictures. JMLR, 3, 1107–1135. [8] Bosch, A., Zisserman, A., & Munoz, X. Image classification using random forests and ferns. In ICCV. [9] Bosch, A., Zisserman, A., & Munoz, X. Representing shape with a spatial pyramid kernel. In CIVR. [10] Boureau, Y., Bach, F., Le Cun, Y., & Ponce, J. Learning mid-level features for recognition. In CVPR