Blockwise Classification: Resolving Inconsistencies with Spectral Correction and Hedged Ridge Regression

Gaobo Hu () och Chunguang Huo ()

Sammanfattning

Image classification systems have been widely applied in smartphones, industrial inspection, and automated devices. However, in real-world scenarios, existing classifiers often produce inconsistent or incorrect predictions due to factors such as image degradation, inter-class similarity, and model instability, thereby reducing the overall performance of the system. To address these issues and enhance the stability and robustness of classification systems under suboptimal conditions, this paper proposes a post-processing error correction framework based on existing classification outputs. By integrating classification information, the proposed method can effectively identify and correct systematic prediction errors, thereby improving the reliability and accuracy of the final decision.

In the experiments, classifiers were constructed using different strategies, including KNN and SVM. These classifiers were used to recognize images, while principal eigenvalue analysis was employed to extract key discriminative features. Furthermore, Hybrid Ridge Regression (HRR) was applied to enhance prediction
stability and accuracy.

The experiments were conducted on a standard image dataset, where recognition errors mainly stemmed from the classifiers’ prediction uncertainty under real-world conditions. A block-wise structure was adopted during the classification process. The results demonstrate that the proposed method significantly improves classification accuracy and exhibits strong robustness, especially when training data is limited or predictions are unstable. Moreover, the method is computationally efficient and structurally lightweight, making it suitable for both standard computing platforms and resource-constrained devices.

In summary, the proposed classification strategy achieves a well-balanced trade-off among accuracy, robustness, and efficiency. Its flexible structure allows integration with various classification models, demonstrating strong adaptability and practical application value.

Handledare: Michael Lentmaier (EIT) och Johan Thunberg (EIT)
Examinator: Thomas Johansson (EIT)