Animal Classification Using Appearance-Based, Model-Based and Texture-Based Methods

Animal recognition and their classification have become increasingly popular area in pattern recognition and computer vision. Distinguishing between images of different animals and different species as means of classifying animals is an easy task for humans. However, it is difficult to distinguish animal species automatically even in simple cases such as distinguishing cats and dogs. Animal bodies easily get disfigured, they can appear in images in a way that they self-occlude and often the background in such images could be very complex and noisy. Furthermore, just as all objects in an image, illumination may vary from image to image, the dimension and viewpoints may also differ. There has been attempts to carry out animal recognition from images but this problem has gained not enough attention. In the literature, there are some visual animal biometrics algorithms categorizing specific animal species such as zebra, elephant, chimpanzees, tiger, whales, pet animals like dogs, etc. Moreover, there are a few publicly available animal face databases including the facial images of different animals such as LHI Animal Faces, HiT and KTH Databases. In this thesis, we studied on different animal face images to classify different animal species. We implemented appearance-based, texture-based and model-based feature extraction methods to categorize animals from their faces and a comparative study is performed at the end of the thesis. Keywords: Animal classification, visual animal biometrics, appearance-based methods, model-based methods, texture-based methods.