Self-supervised sparse coding scheme for image classification based on low rank representation.

Area of Science:

• Computer Science
• Machine Learning
• Artificial Intelligence

Background:

• Sparse Representation-based Classification (SRC) is successful in image classification.
• Conventional SRC methods ignore sample dependencies, leading to quantization sensitivity and misclassification.
• Similar samples may be incorrectly categorized due to limitations in existing coding schemes.

Purpose of the Study:

• To propose a novel self-supervised sparse representation approach for improved image classification.
• To address the limitations of conventional SRC by incorporating sample dependencies.
• To enhance classification accuracy through a self-supervised coding model that preserves local structure.

Main Methods:

• Exploiting manifold structure using low-rank representation.
• Establishing a self-supervised sparse coding model based on the low-rank representation matrix.
• Developing a numerical algorithm using the alternating direction method of multipliers (ADMM) for approximate solutions.

Main Results:

• The proposed approach effectively preserves the local structure of codings for similar samples.
• Experiments on public datasets demonstrate the effectiveness of the novel method.
• The approach shows improved efficiency and accuracy compared to state-of-the-art methods.

Conclusions:

• The developed self-supervised sparse representation method offers a significant advancement in image classification.
• Preserving local structure and sample dependencies leads to more robust and accurate classification.
• The proposed method provides an effective and efficient solution for image classification challenges.