Email this article 
Open Access
Subscription or Fee Access
A Survey on Mining of Weakly Labeled Human Websites Images and Correct Face Annotation
Abstract
Facial recognition is a sort of biometric programming application that can distinguish a particular individual in a computerized picture by investigating and looking at examples. Face recognition is used for security purpose in the area of computer vision and also used in the course of the last few years as a result of its request in various domains serving as real world administration systems and multimedia. Mining of human facial pictures from websites and face annotation related to face identification for an input image is testing assignment for scientists. Image retrieval based on content require a systems for clients to query pictures by their visual content but this make it hard for applicants to formulate query as well as leads to unsatisfied retrieval results. Hence facial image annotation introduced. The goal of annotation to web facial images is to naturally allot essential keywords to images so users are ready to query images with keywords and naturally detect human faces from photo image after that provides remote name to the faces with corresponding human names. Here paper provides methods used for facial image annotation. We get weakly labeled facial on world wide web many times therefore to annotate these image correctly is vital and has found many applications in online photo album management, real world management system and in multimedia to correctly identify the person in video. It asserts towards a strong need of a system that effectively performs mining and annotation of these weakly labeled images. This paper gives overview about techniques or methods used for facial annotation.
Keywords
References
Y. Tian, W. Liu, F. Wen,R. Xiao, and X. Tang, “A Face Annotation Framework with Partial Clustering and Interactive Labeling,” Proc. IEEE Conference Computer Vision and Pattern Recognition (CVPR), year 2007.
Z. Cao X. Tang,, Q. Yin, and J. Sun, “Face Recognition with Learning-Based Descriptor,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp-2707-2714, year 2010.
Stone, T., Darrell, Z., Zickler, T: Autotagging facebook: Social network context improves photo annotation. In: CVPR Workshop, year 2008.
S. Satoh, and T. Kanade, Y. Nakamura, “Name-It: Naming and Detecting Faces in News Videos,” IEEE conf. Multimedia, no.1, vol. 6, pp. 22-35, Jan.-Mar. 1999.
A.W.M. Smeulders, S. Santini, M. Worring, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Transaction. Pattern Analysis and Machine Intelligence, no. 12, vol. 22, pp. 1349-1380, Dec. 2000.
D. Ozkan and P. Duygulu, “A Graph Based Approach for Naming Faces in News Photos,” Proc. IEEE computer science Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1477-1482, year 2006.
M. Guillaumin, J. Verbeek, T. Mensink, and C. Schmid, “Automatic Face Naming with Caption-Based Supervision,” Proc. IEEE Conference Computer Vision and Pattern Recognition (CVPR), year 2008.
M. Guillaumin, J. Verbeek, T. Mensink, and C. Schmid, “Face Recognition from Caption-Based Supervision,” Int’l J. Computer Vision and pattern recognition, vol. 96, pp. 64-82, year 2011.
T. Mensink and J.J. Verbeek, “Improving People Search Using Query Expansions,” Proc. 10th European Conference. Computer Vision (ECCV), vol. 2, pp. 86-99, year 2008.
T.L. Berg, A.C. Berg, M. Maire, J. Edwards, R. White, E.G. Learned-Miller, Y.W. Teh, and D.A. Forsyth, “Names and Faces in the News,” Proc. IEEE computer science Conf. Computer Vision and Pattern Recognition (CVPR), pp. 848-854, year 2004.
M. Zhao, H. Adam, J. Yagnik, and D. Bau, “Large Scale Learning and Recognition of Faces in Web Videos,” Proc. IEEE Eighth Int’ l Conf. Automatic Face and Gesture Recognition (FG), pp. 1-8, year 2008.
Z. WuJ. Sun,, Q. Ke, and H.-Y. Shum, “Scalable Face Image Retrieval with Identity-Based Quantization and Multi-Reference.Re-Ranking,” Proc. IEEE Conference. Computer Vision and Pattern Recognition (CVPR), pp. 3469-3476, year 2010.
D. Wang, S.C.H. Hoi, Y. He, andJ. Zhu, “Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding,” Proc. 18th ACM Int’l Conference. Multimedia (Multimedia), pp. 354-362, year 2011.
D. Wang, Y. He, and S.C.H. Hoi, “A Unified Learning Framework for Auto Face Annotation by Mining Web Facial Images,” Proc.21st ACM Int’l Conf. Information and Knowledge Management (CIKM), pp. 1382-1401, 2012.
Dayong Wang, Ying He,and Jianke Zhu, “ Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 2, January 2014.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.