A New Supervised Clustering Framework Using Multi Discriminative Parts and Expectation–Maximization Approach for a Fine-Grained Animal Breed Classification (SC-MPEM)

Abstract

Fine-grained image classification is active research in the field of computer vision. Specifically, animal breed classification is an arduous task due to the challenges in camera traps images like occlusion, camouflage, poor illumination, pose variation, etc. In this paper, we propose a fine-grained animal breed classification model using supervised clustering based on Multi Part-Convolutional Neural Network (MP-CNN) and Expectation–Maximization (EM) clustering. The proposed model follows a straightforward pipeline that combines the deep feature extraction using the CNN pre-trained on ImageNet and classifies unsupervised data using EM clustering. Further, we also propose a multi discriminative part selection and detection for the precise classification of animal breeds without using bounding box and annotations on both training and testing phases. The model is tested on several benchmark datasets for animals, including the largest camera trap Snapshot Serengeti dataset and has achieved a cumulative accuracy of 98.4%. The results from the proposed model strengthen the belief that supervised training of deep CNN on a large and versatile dataset, extracts better features than most of the traditional approaches, even for the unsupervised tasks.