A Logarithmic Function-Based Novel Representation Algorithm for Image Classification

Salient feature extraction is an important task in image classification and recognition. Although classification techniques focus on the bright part of an image, many pixels of the image are of similar saliency. To address the issue, this paper proposes the logarithmic function-based novel representation algorithm (LFNR) to apply a novel representation for each image. The original and novel representations were fused to improve the classification accuracy. Experimental results show that, thanks to the simultaneous use of original and novel representations, the test samples could be better classified. The classification algorithms coupled with the LFNR all witnessed lower error rates than the original algorithms. In particular, the collaboration representation-based classification coupled with the LFNR significantly outperformed the other sparse representation algorithms, such as homotopy, primal augmented Lagrangian method (PALM), and sparse reconstruction by separable approximation algorithm (SpaRSA). The no-parameter property of the LFNR is also noteworthy.

Automated summary

In this study, a novel representation algorithm based on logarithmic function was proposed to enhance image classification accuracy. The fusion of original and novel representations led to lower error rates in classification algorithms coupled with LFNR, especially outperforming other sparse representation algorithms. The no-parameter property of LFNR was also highlighted as a notable feature.