Animal Recognition using Local Binary Pattern Histogram (LBPH)
In this paper, we have analysed Local Binary Pattern Histogram which is Commonly used for image recognition, especially face recognition. The experiment is conducted on data set build by us which contains 90 images of each class. There are 2 classes namely Okapi which is a rare animal which is to be identified. The training dataset is a collection of images of okapi. It has been observed that there are various factors that act as challenges in the process of image recognition like illumination, size, orientation, etc. In recent years, a new view-based approach to image recognition has been developed. Here a class will contain all images of one particular animal that is, okapi. The goal is to implement the automated system for recognition of Okapi using their images and recording the amount of time the system takes to perform recognition. This can be easily scaled to many more classes or animals. The procedure of the image recognition system is as follows. First, parameters are set; namely radius, neighbours, grid-X and grid-Y . Second, the algorithm is trained on training set of images and histograms are generated. Finally, these histograms will be used to predict test images. Here we keep the total number of images constant and divide them into different ratios of training and testing images.
Keywords - Histogram, Binary pattern, LBPH.