### Abstract

Original language | English |
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Title of host publication | Proceedings of the British Machine Vision Conference 2012 |

Place of Publication | UK |

Publisher | BMVA Press |

Pages | 1-11 |

Volume | 1 |

ISBN (Print) | 1901725464 |

DOIs | |

Publication status | Published - 2012 |

Event | Hierarchical sparse spectral clustering for image set classification - UK Duration: 1 Jan 2012 → … |

### Conference

Conference | Hierarchical sparse spectral clustering for image set classification |
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Period | 1/01/12 → … |

### Fingerprint

### Cite this

*Proceedings of the British Machine Vision Conference 2012*(Vol. 1, pp. 1-11). UK: BMVA Press. https://doi.org/10.5244/C.26.51

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*Proceedings of the British Machine Vision Conference 2012.*vol. 1, BMVA Press, UK, pp. 1-11, Hierarchical sparse spectral clustering for image set classification, 1/01/12. https://doi.org/10.5244/C.26.51

**Hierarchical sparse spectral clustering for image set classification.** / Mahmood, Arif; Mian, Ajmal.

Research output: Chapter in Book/Conference paper › Conference paper

TY - GEN

T1 - Hierarchical sparse spectral clustering for image set classification

AU - Mahmood, Arif

AU - Mian, Ajmal

PY - 2012

Y1 - 2012

N2 - We present a structural matching technique for robust classification based on image sets. In set based classification, a probe set is matched with a number of gallery sets and assigned the label of the most similar set. We represent each image set by a sparse dictionary and compute a similarity matrix by matching all the dictionary atoms of the gallery and probe sets. The similarity matrix comprises the sparse coding coefficients and forms a fully connected directed graph. The nodes of the graph are the dictionary atoms and the edges are the sparse coefficients. The graph is converted to an undirected graph with positive edge weights and spectral clustering is used to cut the graph into two balanced partitions using the normalized cut algorithm. This process is repeated until the graph reduces to critical and non-critical partitions. A critical partition contains atoms with the same gallery label along with one or more probe atoms whereas a non-critical partition either consists of only probe atoms or atoms with multiple gallery labels with no probe atom. Using the critical partitions, we define a novel set based similarity measure and assign the probe set the label of the gallery set with maximum similarity. The proposed algorithm is applied to image set based face recognition using two standard databases. Comparison with existing techniques shows the validity and robustness of our algorithm in the presence of outlier images.

AB - We present a structural matching technique for robust classification based on image sets. In set based classification, a probe set is matched with a number of gallery sets and assigned the label of the most similar set. We represent each image set by a sparse dictionary and compute a similarity matrix by matching all the dictionary atoms of the gallery and probe sets. The similarity matrix comprises the sparse coding coefficients and forms a fully connected directed graph. The nodes of the graph are the dictionary atoms and the edges are the sparse coefficients. The graph is converted to an undirected graph with positive edge weights and spectral clustering is used to cut the graph into two balanced partitions using the normalized cut algorithm. This process is repeated until the graph reduces to critical and non-critical partitions. A critical partition contains atoms with the same gallery label along with one or more probe atoms whereas a non-critical partition either consists of only probe atoms or atoms with multiple gallery labels with no probe atom. Using the critical partitions, we define a novel set based similarity measure and assign the probe set the label of the gallery set with maximum similarity. The proposed algorithm is applied to image set based face recognition using two standard databases. Comparison with existing techniques shows the validity and robustness of our algorithm in the presence of outlier images.

U2 - 10.5244/C.26.51

DO - 10.5244/C.26.51

M3 - Conference paper

SN - 1901725464

VL - 1

SP - 1

EP - 11

BT - Proceedings of the British Machine Vision Conference 2012

PB - BMVA Press

CY - UK

ER -