Computer Science Colloquium Series - Fall 2012
Hierarchical Sparse Spectral Clustering for Image Set Classification
by Dr. Arif Mahmood, Research Assistant Professor, University of Western Australia
Date: Friday, September 14, 2012
Time: 3:00 pm
Venue: Smart Room, CS Department, LUMS
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 noncritical 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.
Arif Mahmood is a Research Assistant Professor at the University of Western Australia. Prior to this, he was an Assistant Professor of Computer Science at Punjab University College of Information Technology (PUCIT), University of the Punjab, Lahore. Dr. Mahmood completed his MS and PhD from LUMS in 2003 and 2011 respectively, and won the gold medal for highest academic achievement in MS. His research interests broadly span the area of computer vision and image processing. He won the best paper award at International Conference on Machine Vision, Islamabad in 2010.