Image Classification Using Singular Value Decomposition and Optimization
This is an incremental approach for resource-constrained environments, addressing a specific domain problem in animal breed classification.
The study tackled image classification of cat and dog breeds using fur color by applying Singular Value Decomposition and Sequential Quadratic Programming, achieving 69% accuracy with a low-rank approximation.
This study investigates the applicability of Singular Value Decomposition for the image classification of specific breeds of cats and dogs using fur color as the primary identifying feature. Sequential Quadratic Programming (SQP) is employed to construct optimally weighted templates. The proposed method achieves 69% accuracy using the Frobenius norm at rank 10. The results partially validate the assumption that dominant features, such as fur color, can be effectively captured through low-rank approximations. However, the accuracy suggests that additional features or methods may be required for more robust classification, highlighting the trade-off between simplicity and performance in resource-constrained environments.