Relaxed Collaborative Representation for Pattern Classification

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Authors Meng Yang, Lei Zhang, David Zhang and Shenlong Wang
Journal/Conference Name 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)
Paper Category
Paper Abstract Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representation and classification, in this paper we present a novel relaxed collaborative representation (RCR) model to effectively exploit the similarity and distinctiveness of features. In RCR, each feature vector is coded on its associated dictionary to allow flexibility of feature coding, while the variance of coding vectors is minimized to address the similarity among features. In addition, the distinctiveness of different features is exploited by weighting its distance to other features in the coding domain. The proposed RCR is simple, while our extensive experimental results on benchmark image databases (e.g., various face and flower databases)show that it is very competitive with state-of-the-art image classification methods
Date of publication 2012
Code Programming Language MATLAB

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