TMTCPT: The Tree Method based on the Taxonomic Categorization and the Phylogenetic Tree for fine-grained categorization. (July 2020)

• Record Type: Journal Article
• Authors: Bameri, Fateme; Pourreza, Hamid-Reza; Taherinia, Amir-Hossein; Aliabadian, Mansour; Mortezapour, Hamid-Reza; Abdilzadeh, Raziyeh
• Is Part Of: Bio systems. Volume 195(2020)
• Publication Date: 2020-07
• Subjects: Fine-grained categorization -- Taxonomic tree for fine-grained categorization -- CUB-200-2011 birds classification -- Tree algorithm for bird categorization; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2020.104137
• Languages: English
• ISSNs: 0303-2647

Abstract

Abstract : 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.