Semi-supervised spectral clustering for image set classification

Arif Mahmood, Ajmal Mian, Robyn Owens

Research output: Chapter in Book/Conference paperConference paperpeer-review

44 Citations (Scopus)


© 2014 IEEE. We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to define a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a proximity matrix is efficiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and finer clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an efficient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and final classification results. Experiments on five standard datasets and comparison with seven existing techniques show the efficacy of our algorithm.
Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781479951178
Publication statusPublished - 2014
Event2014 IEEE Conference on Computer Vision and Pattern Recognition - USA, Columbus, United States
Duration: 23 Jun 201428 Jun 2014
Conference number: 114804


Conference2014 IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
Country/TerritoryUnited States


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