Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these methods, we offer a new framework for fine-grained categorization. Our tree method, named “TMTCPT”, is based on the taxonomic categorization, phylogenetic tree, and convolutional neural network classifiers. The word “taxonomic” has been derived from “taxonomical categorization” that categorizes objects and visual features and performs a prominent role in this category. It presents a hierarchical categorization that leads to multiple classification levels; the first level includes the general visual features having the lowest similarity level, whereas the other levels include visual features strikingly similar, as they follow top-bottom hierarchy. The phylogenetic tree presents the phylogenetic information of organisms. The convolutional neural network classifiers can classify the categories precisely. In this study, the researchers created a tree to increase classification accuracy and evaluated the effectiveness of the method by examining it on the challenging CUB-200-2011 dataset. The study results demonstrated that the proposed method was efficient and robust. The average classification accuracy of the proposed method was 88.34%, being higher than those of all the previous methods.

细粒度分类是机器视觉中最具挑战性的问题之一。最近，提出的方法已经基于卷积神经网络，极大地提高了分类的准确性。受这些方法的启发，我们提供了一种用于细分类的新框架。我们的树方法称为“ TMTCPT”，它基于分类学分类，系统树和卷积神经网络分类器。“分类”一词源自“分类分类”，该分类将对象和视觉特征分类，并在此类别中发挥重要作用。它提出了导致多个分类级别的分层分类。第一层包括具有最低相似度的一般视觉特征，而其他级别的视觉功能则遵循自上而下的层次结构，因此极为相似。系统发育树显示了生物的系统发育信息。卷积神经网络分类器可以对类别进行精确分类。在这项研究中，研究人员创建了一棵树来提高分类准确性，并通过在具有挑战性的CUB-200-2011数据集上对其进行了评估来评估该方法的有效性。研究结果表明，该方法是有效且鲁棒的。提出的方法的平均分类准确度为88.34％，高于所有以前的方法。在这项研究中，研究人员创建了一棵树来提高分类准确性，并通过在具有挑战性的CUB-200-2011数据集上对其进行了评估来评估该方法的有效性。研究结果表明，该方法是有效且鲁棒的。提出的方法的平均分类准确度为88.34％，高于所有以前的方法。在这项研究中，研究人员创建了一棵树来提高分类准确性，并通过在具有挑战性的CUB-200-2011数据集上对其进行了检查来评估该方法的有效性。研究结果表明，该方法是有效且鲁棒的。提出的方法的平均分类准确度为88.34％，高于所有以前的方法。