Enhanced Animal Image Classification Using Optimized Support Vector Machine

Animal image classification is a crucial task in various domains, including wildlife monitoring, biodiversity conservation, and veterinary research. Traditional classification methods often struggle with variations in animal species, poses, and environmental conditions. This paper proposes an efficient approach using Support Vector Machine (SVM) for accurate animal image classification. SVM is a powerful supervised learning algorithm that effectively handles high-dimensional data and ensures optimal separation between different animal categories. The objective is to develop a robust model capable of classifying different animal species from images with high accuracy. The dataset used for training and testing consists of a large collection of labeled images from diverse animal species, including mammals, birds, reptiles, and amphibians. Experimental results show that the SVM-based classifier achieves competitive performance in terms of classification accuracy.The proposed method demonstrates that SVM is a viable solution for animal image classification, with the potential for real-time applications in monitoring animal populations and automating wildlife surveys.