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Taxonomic Classification of Objects with Convolutional Neural Networks

it is difficult to build a CNN model that can classify many classes at once. Therefore, this study does not want to make many classes recognizable at once using only one model but by taxonomic classification. This study suggests a method of dividing the large number of classes into different steps of each step using Taxonomic classification. We propose a method of classifying a large number of classes by dividing them into models for each step using taxonomic classification. Our method uses taxonomic classification to distribute the weights required for training and test step by step. This will save a lot of time than creating a one-level model. In addition, to detect objects in never trained categories, the result may come up to a certain step without retraining the model. This shows that part of the model can be recycled. In this study, we presented a way to distinguish large numbers of classes using taxonomic classification by using multiple datasets, such as PASCAL VOC2012, ILSVRC 2013 image data from ImageNet, and 102 Category Flower Dataset.