Representation-based Algorithm Research Articles

Robust Hierarchical Framework for Image Classification via Sparse Representation

The sparse representation-based classification algorithm has been used for human face recognition. But an image database was restricted to human frontal faces with only slight illumination and expression changes. Cropping and normalization of the face needs to be done beforehand. This paper uses a sparse representation-based algorithm for generic image classification with some intra-class variations and background clutter. A hierarchical framework based on the sparse representation is developed which flexibly combines different global and local features. Experiments with the hierarchical framework on 25 object categories selected from the Caltech101 dataset show that exploiting the advantage of local features with the hierarchical framework improves the classification performance and that the framework is robust to image occlusions, background clutter, and viewpoint changes.

A class of weakly stationary non-Gaussian models

Two models are developed for real-valued, weakly stationary non-Gaussian processes using the spectral representation X ( t ) = ∫ 0 ∞ [ cos ( ν t ) d M 1 ( ν ) + sin ( ν t ) d M 2 ( ν ) ] . The processes M i , i = 1 , 2 , in this representation are (1) square integrable martingales defined as functions H n ( B i ( ν ) , ν ) , ν ≥ 0 , depending on Hermite polynomials of independent Brownian motions B i , i = 1 , 2 , or (2) have increments d M i ( ν ) = G i ( ν ) d B i ( ν ) , ν ≥ 0 , where G i are processes with finite variance independent of B i , i = 1 , 2 . It is shown that the models can be calibrated to any target spectral density. Efficient methods are developed for calculating their higher order statistics. Features and limitations of the proposed models are illustrated by numerical examples. Monte Carlo algorithms for generating non-Gaussian samples based on the models in the paper resemble classical spectral representation-based algorithms for generating stationary Gaussian samples.