Efficient Image Classification via Multiple Rank Regression

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Authors Chenping Hou, Feiping Nie, Dongyun Yi, Yi Wu
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Paper Abstract The problem of image classification has aroused considerable research interests in the field of image processing. Traditional methods often convert an image to a vector and then use vector based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is proposed. Different from traditional vector-based methods, we employ multiple-rank left projecting vectors and right projecting vectors to regress each matrix data to its label for each category. The convergence behavior, initialization, computational complexity and parameter determination are also analyzed. Compared with vector-based regression methods, MRR achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRR performs better for matrix data classification. Promising experimental results on face, object and hand-written digit image classification tasks are provided to show the effectiveness of our method.
Date of publication 2013
Code Programming Language MATLAB
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