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Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

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Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

Taxonomy

Taxonomy

Taxonomy is the science of defining and naming groups of biological organisms based on shared characteristics. It uses a hierarchy of increasingly inclusive categories with Latin names. The smallest units of taxonomy, species and genus, are used to assign a formal, taxonomic name to each species in a system. This classification system, referred to as binomial nomenclature, was formalized by Carolus Linnaeus in the 18th century.
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Updated: Dec 22, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

TMTCPT: The Tree Method based on the Taxonomic Categorization and the Phylogenetic Tree for fine-grained

Fateme Bameri¹, Hamid-Reza Pourreza¹, Amir-Hossein Taherinia¹

¹Faculty of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Machine Vision Lab, Ferdowsi University of Mashhad, Mashhad, Iran.

|May 4, 2020

Summary

A new tree-based method, TMTCPT, enhances fine-grained visual categorization using taxonomic and phylogenetic information with convolutional neural networks. This approach achieved 88.34% accuracy, outperforming previous methods on the CUB-200-2011 dataset.

Keywords:

CUB-200-2011 birds classification Fine-grained categorization Taxonomic tree for fine-grained categorization Tree algorithm for bird categorization

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Fine-grained categorization is a complex machine vision challenge.
Convolutional Neural Networks (CNNs) have significantly improved classification accuracy.
Existing methods require further enhancement for nuanced visual tasks.

Purpose of the Study:

To introduce a novel framework, TMTCPT, for improved fine-grained visual categorization.
To leverage taxonomic categorization and phylogenetic trees within a CNN framework.
To enhance classification accuracy through hierarchical feature analysis.

Main Methods:

Developed a tree-based method (TMTCPT) integrating taxonomic categorization, phylogenetic trees, and CNN classifiers.
Employed hierarchical categorization with multiple classification levels, from general to highly similar features.
Utilized phylogenetic information to represent organismal relationships.

Main Results:

The TMTCPT method demonstrated high efficiency and robustness on the CUB-200-2011 dataset.
Achieved an average classification accuracy of 88.34%.
Outperformed all previously reported methods on the benchmark dataset.

Conclusions:

The proposed TMTCPT framework effectively addresses fine-grained visual categorization challenges.
The integration of taxonomic and phylogenetic structures with CNNs offers a powerful approach.
This method significantly advances the state-of-the-art in fine-grained image classification.