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.

中文翻译：

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一种新的有监督的聚类框架，该框架使用多判别部分和期望最大化方法对细粒动物品种进行分类（SC-MPEM）

细粒度图像分类是计算机视觉领域的活跃研究。具体而言，由于相机陷阱图像的遮挡，伪装，光照差，姿势变化等问题，动物品种分类是一项艰巨的任务。在本文中，我们提出了一种基于监督聚类的细粒度动物品种分类模型部分卷积神经网络（MP-CNN）和期望最大化（EM）聚类。所提出的模型遵循简单的流程，该流程结合了使用ImageNet上预训练的CNN进行深度特征提取，并使用EM聚类对非监督数据进行分类。此外，我们还针对动物品种的精确分类提出了一种多判别部分选择和检测方法，而无需在训练和测试阶段都使用边界框和注释。该模型在动物的多个基准数据集上进行了测试，包括最大的相机陷阱Snapshot Serengeti数据集，累计准确度达到98.4％。所提出模型的结果强化了这样一种信念，即在大型通用数据集上进行有监督的深度CNN训练，即使对于无人监督的任务，也比大多数传统方法具有更好的功能。