Author : Sneha 1
Date of Publication :7th March 2016
Abstract: Now days it has become trend to follow all the celebrities as we consider them as our role models. So instead of searching the images of various celebrities in different websites we can find them in a single website by sorting all the images. Reliable database of images is essential for any image recognition system. Through Internet we find all the required images. These images will serve as samples for automatic recognition system. With these images we do face detection, face recognition, face sorting using various techniques like local binary patterns, haar cascades. We make an overall analysis of the detector. Using opencv we detect and recognize images. Similarity matching is done to check how the images are related to each other. Collection of images is based on user defined templates, which are in web browser environment. With the help of this system one can give their requirement and the image of celebrity is displayed based on that.
Reference :
-
- Michal vagac , Miroslav Melichercik , Matus Marko (2015). Crawling images with web browser support : IEEE 13th International Scientific Conference on Informatics. Informatics’2015 . November 18-20 . poprad . Slovakia
- Junghoo Cho , Hector Garcia-Molina, Lawrence Page (2012). Reprint of : Efficient crawling through URL ordering : Elsevier Journal , Computer Networks 26( 2012 ) 3849-3858.
- Paul Voila , Michael Jones . Rapid Object Detection using a Boosted Cascade of Simple Features . Accepted Conference on Computer Vision and Pattern Recognition 2001.
- Caifeng Chan , Shogang Hong , Robust Facial Recognition using Local Binary Patterns . Image Processing, 2005. ICIP 2005. IEEE International Conference on (Volume:2 )
- Eli shechtman , Michal Irani. Matching Self local similarities across Image and Videos. http://www.wisdom.weizmann.ac.il/~vision/VideoAnalysis /Demos/SelfSimilarities
- V. Ferrari, T. Tuytelaars, and L. V. Gool. Object detection by contour segment networks. In ECCV, May 2006.
- R. Baumgartner , S. Flesca and G. Gottlob, Visual web information extraction with lixto , In Proceedings of the 27th International Conference on Very Large Data Bases, VLDB 01, pages 119-128, San Francisco, CA, USA: Morgan Kaufmann publishers Inc, 2001
- V. Crescenzi , P. Merialdo , and D. Qui, Alfred : Crowd assisted data extraction, In Proceedings of the 22nd International Conference on World Wide Web Companion , WWW 13 Companion, pages 297-300, Republic and Canton of Geneva, Switzerland, 2013.
- T.Furche , G. Gottlob, G.Grasso , C. Schallhart and A. Sellers, Oxpath: A language for scalable data extraction , automation, and crawling on the deep web, The VDLB Journal , 22(1):47-72, Feb. 2013.
- R. Brooks, T. Arbel, D. Precup, Anytime similarity measures for faster alignment, Computer Vision and Image Understanding 110 (3) (2008) 378–389
- T.Grigalis, Towards web-scale structured web data extraction , In proceedings of the Sixth ACM International Conference on Web Search and Data Mining , WSDM 13, pages 753-758, New York, NY, USA:ACM, 2013.
- K. Kanaoka , Y. Fujii , M. Toyama , Ducky : a data extraction system for various structured web documents , In Proceedings of the 18th International Database Engineering & Applications Symposium, IDEAS ‘ 14. Pages 342-347, New York , NY, USA: ACM, 2014
- N. Kushmerick , Wrapper induction : Efficiency and expressiveness, Artificial Intelligence , Vol 118, Issue 1-2 , pages 15-68. Essex, UK: Elsevier Science Publishers Ltd., 2000
- M. Tlo and M. Suzuki, Design and implementation of a facility for wandering and manipulating the structure of on-line data, In Information Science and Applications (ICISA), 2011 International Conference on, pages 1-8, April 2011
- M. Geel , T. Church and M.C . Norrie, Sift : An end – user tool for gathering web content on the go , In Proceedings of the 2012 ACM Symposium on Document Engineering , DocEng 12, pages 181-190, New York, NY, USA: ACM, 2012.
- C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Vision, 1998.
- L. Itti, C. Koch, and E. Niebur. A model of saliencybased visual attention for rapid scene analysis. IEEE Patt. Anal. Mach. Intell., 20(11):1254–1259, November 1998
- H. Greenspan, S. Belongie, R. Gooodman, P. Perona, S. Rakshit, and C. Anderson. Overcomplete steerable pyramid filters and rotation invariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1994.
- Edgar Osuna, Robert Freund, and Federico Girosi. Training support vector machines: an application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1997
- T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, , and T. Poggio. Robust object recognition with cortex-like mechanisms. to appear in PAMI, 2006.
- Di Huang , Caifeng Shan, Mohsen Ardabilian. Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 41, no. 6, november 2011
- Chi-Ho Chan, Josef Kittler, Kieron Messer, Multi scale local Pattern Histograms for Face Recognition. Advances in Biometrics Volume 4642 of the series Lecture Notes in Computer Science pp 809-818
- G. Zhang, X. Huang, S. Z. Li, Y. Wang, and X. Wu. Boosting Local Binary Pattern (LBP)-based face recognition, volume 3338. Springer Berlin / Heidelberg, 2004.
- S. Yan, S. Shan, X. Chen, and W. Gao. Locally assembled binary (lab) feature with feature-centric cascade for fast and accurate face detection. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.
- B. Fröba and A. Ernst. Face detection with the modified census transform. In Sixth IEEE Int. Conference on Automatic Face and Gesture Recognition, pages 91–96, 2004
- H. Jin, Q. Liu, H. Lu, and X. Tong. Face detection using improved lbp under bayesian framework. In Third Int. Conference on Image and Graphics, pages 306–309, 2004.
- M. Heikkilä, M. Pietikäinen, and C. Schmid. Description of interest regions with local binary patterns. Pattern Recognition, 42(3):425– 436, 2009.