Exemplar Selection Using Collaborative Neighbor Representation

Retrieving the most relevant exemplars in image databases has been a difficult task. Most of exemplar selection methods were proposed and developed to work with a specific classifier. Research in exemplar selection is targeting schemes that can benefit a wide range of classifiers. Recently, Sparse Modeling Representative Selection (SMRS) method has been proposed for selecting the most relevant instances. SMRS is based on data self-representation in the sense that it estimates a coding matrix using a codebook set to the data themselves. The matrix coefficients are estimated using block sparsity constraint. In this paper, we propose a coding scheme based on a two stage Collaborative Neighbor Representation in the matrix of coefficients is estimated without any explicit sparse coding. For the second stage, we introduce two schemes for sample pruning in the second stage. Experiments are conducted on summarizing two video movies. We also provide quantitative performance evaluation via classification on the selected prototypes. To this end, one face dataset, one handwritten digits dataset, and one object dataset are used. These experiments showed that the proposed method can outperform state-of-the art methods including the SMRS method.