To Improve Content Based Face Retrieval By Creating Semantic Code Words

Melam Srikanth, A Ramamurthy, K.T.V Subbarao


The importance and the complete amount of human face photos make manipulations e.g., search and mining of large-scale human face images a really vital research problem and allow many real world applications. We aim to make use of automatically detected human attributes that contain semantic prompts of the face photos to improve content based face retrieval by constructing semantic code words for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework we propose two orthogonal methods named attribute-enhanced sparse coding and attribute embedded inverted indexing to perk up the face retrieval in the offline and online stages. We examine the efficiency of different attributes and vital factors necessary for face retrieval. The purpose in this paper is to deal with one of the imperative and challenging problems large-scale content-based face image retrieval. Given a uncertainty face image content-based face image retrieval seeks to find similar face images from a large image database. It is and facilitates equipment for many applications including automatic face annotation crime investigation etc.



Face image, human attributes, content-based image retrieval


Y.-H. Lei, Y.-Y. Chen, L. Iida, B.-C. Chen, H.-H. Su, and W. H. Hsu, “Photo search by face positions and facial attributes on touch devices,” ACM Multimedia, 2011.

D. Wang, S. C. Hoi, Y. He, and J. Zhu, “Retrieval-based face annotation by weak label regularized local coordinate coding,” ACM Multimedia, 2011.

U. Park and A. K. Jain, “Face matching and retrieval using soft biometrics,” IEEE Transactions on Information Forensics and Security, 2010.

Z. Wu, Q. Ke, J. Sun, and H.-Y. Shum, “Scalable face image retrieval with identity-based quantization and multi-reference re-ranking,” IEEE Conference on Computer Vision and Pattern Recognition, 2010.

B.-C. Chen, Y.-H. Kuo, Y.-Y. Chen, K.-Y. Chu, and W. Hsu, “Semisupervised face image retrieval using sparse coding with identity constraint,” ACM Multimedia, 2011.

M. Douze and A. Ramisa and C. Schmid, “Combining Attributes and Fisher Vectors for Efficient Image Retrieval,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.

N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, “Describable visual attributes for face verification and image search,” in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Special Issue on Real-World Face Recognition, Oct 2011.

W. Scheirer, N. Kumar, K. Ricanek, T. E. Boult, and P. N. Belhumeur, “Fusing with context: a bayesian approach to combining descriptive attributes,” International Joint Conference on Biometrics, 2011.

B. Siddiquie, R. S. Feris, and L. S. Davis, “Image ranking and retrieval based on multi-attribute queries,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.

W. Scheirer and N. Kumar and P. Belhumeur and T. Boult, “Multi- Attribute Spaces: Calibration for Attribute Fusion and Similarity Search,” IEEE Conference on Computer Vision and Pattern Recognition, 2012.

G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” University of Massachusetts, Amherst, Tech. Rep. 07-49, October 2007.

N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, “Attribute and simile classifiers for face verification,” International Conference on Computer Vision, 2009.

T. Ahonen, A. Hadid, and M. Pietikainen, “Face recognition with local binary patterns,” European Conference on Computer Vision, 2004. [14] J. Zobel and A. Moffat, “Inverted files for text search engines,” ACM Computing Surveys, 2006.

A. Gionis, P. Indyk, and R. Motwani, “Similarity search in high dimensions via hashing,” VLDB, 1999.

J. Sivic and A. Zisserman, “Video google: A text retrieval approach to object matching in videos,” International Conference on Computer Vision, 2003.

D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 2003.

O. Chum, J. Philbin, J. Sivic, M. Isard and A. Zisserman, “Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval,” IEEE International Conference on Computer Vision, 2007.

L. Wu, S. C. H. Hoi, and N. Yu, “Semantics-preserving bag-of-words models and applications,” Journal of IEEE Transactions on image processing, 2010.

Y.-H. Kuo, H.-T. Lin, W.-H. Cheng, Y.-H. Yang, and W. H. Hsu,“Unsupervised auxiliary visual words discovery for large-scale image object retrieval,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.



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