Abstract:
: This study focuses on optimizing Siamese Neural Networks using various distance metrics to enhance
trademark image similarity detection. Traditional Euclidean methods often fail to detect subtle visual differences,
leading to less accurate outcomes. This research incorporates Chi-Squared and Manhattan metrics into the network,
in addition to the conventional Euclidean metric. Using 255 trademark images across five classes, triplet samples
were created for training and evaluation. The models, which utilized the CNN Xception architecture and a triplet loss
function, were trained separately with each distance metric. Performance was assessed comprehensively via multiple
metrics, including accuracy, precision, recall, and F1-Score, to ensure robust evaluation. The results indicated that
the Chi-Squared metric significantly outperformed the others, achieving an impressive accuracy of 0.96. In
comparison, the Manhattan and Euclidean metrics achieved accuracies of 0.74 and 0.92, respectively. Notably, the
Chi-Squared metric improved accuracy by approximately 4.35% compared to the Euclidean metric. These findings
underscore the critical importance of selecting suitable distance metrics for image similarity tasks, as the choice of
metric can substantially impact performance. The Chi-Squared metric was particularly effective due to its sensitivity
to variations in features such as color and texture, which are often pivotal in trademark images. This research
demonstrates the substantial benefits of incorporating advanced distance metrics into deep learning models for
trademark similarity detection, providing a more accurate and reliable approach. By highlighting the effectiveness of
the Chi-Squared metric, this study paves the way for future research to further refine these metrics and explore their
applications in various domains requiring precise image similarity analysis. Future studies may also consider
integrating additional metrics or hybrid approaches to further enhance performance and applicability in diverse
contexts.
