Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Automated annotation of human faces in family albums
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Naming every individual in news video monologues
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient propagation for face annotation in family albums
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Transactions on Knowledge and Data Engineering
A tutorial on spectral clustering
Statistics and Computing
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Graph sparsification by effective resistances
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Prototype vector machine for large scale semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Convex experimental design using manifold structure for image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Mining weakly labeled web facial images for search-based face annotation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Retrieval-based face annotation by weak label regularized local coordinate coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Robust outlier detection using commute time and eigenspace embedding
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Face Annotation Using Transductive Kernel Fisher Discriminant
IEEE Transactions on Multimedia
Learning to name faces: a multimodal learning scheme for search-based face annotation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Recently, the task of unsupervised face-name association has received a considerable interests in multimedia and information retrieval communities. It is quite different with the generic facial image annotation problem because of its unsupervised and ambiguous assignment properties. Specifically, the task of face-name association should obey the following three constraints: (1) a face can only be assigned to a name appearing in its associated caption or to null; (2) a name can be assigned to at most one face; and (3) a face can be assigned to at most one name. Many conventional methods have been proposed to tackle this task while suffering from some common problems, eg, many of them are computational expensive and hard to make the null assignment decision. In this paper, we design a novel framework named face-name association via commute distance (FACD), which judges face-name and face-null assignments under a unified framework via commute distance (CD) algorithm. Then, to further speed up the on-line processing, we propose a novel anchor-based commute distance (ACD) algorithm whose main idea is using the anchor point representation structure to accelerate the eigen-decomposition of the adjacency matrix of a graph. Systematic experiment results on a large scale and real world image-caption database with a total of 194,046 detected faces and 244,725 names show that our proposed approach outperforms many state-of-the-art methods in performance. Our framework is appropriate for a large scale and real-time system.