A rank-order distance based clustering algorithm for face tagging

We present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity between two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face, we generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance between extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency.

[1]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[2]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[3]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[5]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[6]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[7]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[8]  Vipin Kumar,et al.  Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data , 2003, SDM.

[9]  Mingjing Li,et al.  Automated annotation of human faces in family albums , 2003, MULTIMEDIA '03.

[10]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[11]  Benjamin B. Bederson,et al.  Semi-Automatic Image Annotation Using Event and Torso Identification , 2004 .

[12]  Mor Naaman,et al.  Leveraging context to resolve identity in photo albums , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[13]  Dit-Yan Yeung,et al.  Locally Linear Models on Face Appearance Manifolds with Application to Dual-Subspace Based Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Ramesh C. Jain,et al.  Automatic Person Annotation of Family Photo Album , 2006, CIVR.

[15]  Yuandong Tian,et al.  A Face Annotation Framework with Partial Clustering and Interactive Labeling , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Yuandong Tian,et al.  EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking , 2007, CHI.

[17]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[18]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[19]  Tsuhan Chen,et al.  Clothing cosegmentation for recognizing people , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Wen Gao,et al.  Manifold-Manifold Distance with application to face recognition based on image set , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Gang Hua,et al.  Which faces to tag: Adding prior constraints into active learning , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Hakan Cevikalp,et al.  Face recognition based on image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.