Paper Info
Title
Hierarchical Sparse Spectral Clustering For Image Set Classification
Abstract
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.
Year
DOI
Venue
2012
10.5244/C.26.51
PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2012
Field
DocType
Citations 
Canopy clustering algorithm,Spectral clustering,Pattern recognition,Correlation clustering,K-SVD,Similarity measure,Set partitioning in hierarchical trees,Computer science,Sparse approximation,Directed graph,Artificial intelligence
Conference
3
PageRank 
References 
Authors
0.39
15
2
Name
Order
Citations
PageRank
Arif Mahmood138733.58
Ajmal Mian2587.53