Review on Animal Classification Algorithms

Review on Animal Classification Algorithms

Author Name : Dr. S. V. Viraktamath, Vidya, Jahnavi R, Abhay S Bhat, Satvik Nayak

ABSTRACT

There has been a rapid increase in the amount of image data collected in recent years. This emphasizes the necessity of automatic image processing and computer vision techniques like picture categorization, object identification, and face recognition, among others, to make image-related activities easier. One of the most often encountered decision-making tasks in human activity is image classification. When an object must be allocated to a preset cluster or class based entirely on a number of discovered qualities connected with that object, a classification challenge arises. Animals, plants, text texts, economic data, and other objects can all be classified using various classification systems. Different classification methods and methodologies can be used to classify animals, plants, text texts, economic data, and more. A few methods are discussed in this study that can recognize and classify a wide range of animal species. Visual scene tracking for animals is one of the most active research subjects in computer vision (CV) at the time. Additionally, manually classifying animals is time-consuming and unpleasant. As a result, the focus of this study is on a few algorithms that are used to solve animal classification problems.

Keywords: Animal Classification, CNN, ANN, SVM, KNN, ReLU , ScSPM, DPM.