  In this work, we propose a method for the classification of animal in images.
Initially, a graph cut based method is used to perform segmentation in order to
eliminate the background from the given image. The segmented animal images are
partitioned in to number of blocks and then the color texture moments are
extracted from different blocks. Probabilistic neural network and K-nearest
neighbors are considered here for classification. To corroborate the efficacy
of the proposed method, an experiment was conducted on our own data set of 25
classes of animals, which consisted of 4000 sample images. The experiment was
conducted by picking images randomly from the database to study the effect of
classification accuracy, and the results show that the K-nearest neighbors
classifier achieves good performance.
