Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning

Dogs are one of the most common domestic animals. Due to a large number of dogs, there are several issues such as population control, decrease outbreak such as Rabies, vaccination control, and legal ownership. At present, there are over 180 dog breeds. Each dog breed has specific characteristics and health conditions. In order to provide appropriate treatments and training, it is essential to identify individuals and their breeds. The paper presents the classification methods for dog breed classification using two image processing approaches 1) conventional based approaches by Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) 2) the deep learning based approach by using convolutional neural networks (CNN) with transfer learning. The result shows that our retrained CNN model performs better in classifying a dog breeds. It achieves 96.75% accuracy compared with 79.25% using the HOG descriptor.