Comparative Analysis of Image Classification Methods on Cat Breeds and Behavior using Machine Learning Techniques

Abstract

This research investigates image classification techniques applied to two distinct datasets related to cats. The primary focus is on addressing the problem of accurately classifying cat breeds and cat behavior. This research focuses on the comparative analysis of both deep learning and machine learning techniques. The techniques are categorized as use of Transfer learning on deep learning models, Transfer learning on machine learning algorithms, and Teachable machine pre-trained model. Transfer learning has gained popularity as one of the employed techniques, in inception V3 for classifying images. It requires re-utilizing an existing model, for a new model by applying a small-scale dataset to pace up training and enhance overall performance. Five different methodologies are explored: Convolutional Neural Networks using Google Inception-V3 model, Convolutional Neural Networks on top of Google Inception-V3 model with K-fold cross-validation, Random Forest on Inception V3 features, Support Vector Machine on Inception V3 features, and Teachable Machine model. The study aims to compare the performance of these methodologies in terms of accuracy, F1 score, and ROC-AUC score. The research results show that each approach has levels of effectiveness with various algorithms and models showing accuracy and F1 scores in classifying both cat breeds and behaviors. These findings offer information, on image classification, in datasets related to cats helping to improve the precision of identifying cat breeds and behaviors.