Flower Image Classification Using Deep Convolutional Neural Network
View/ Open
File version
Accepted Manuscript (AM)
Author(s)
Alipour, N
Tarkhaneh, O
Awrangjeb, M
Tian, H
Griffith University Author(s)
Year published
2021
Metadata
Show full item recordAbstract
These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing ...
View more >These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing by normalizing and resizing of our images and then fed them to our fine-tuned pretrained model. We divided our dataset to three sets of train, validation, and test. We could achieve the accuracy of 98.6% for 50 epochs which is better than other deep-learning based methods for the same dataset in the study.
View less >
View more >These days deep learning methods play a pivotal role in complicated tasks, such as extracting useful features, segmentation, and semantic classification of images. These methods had significant effects on flower types classification during recent years. In this paper, we are trying to classify 102 flower species using a robust deep learning method. To this end, we used the transfer learning approach employing DenseNet121 architecture to categorize various species of oxford-102 flowers dataset. In this regard, we have tried to fine-tune our model to achieve higher accuracy respect to other methods. We performed preprocessing by normalizing and resizing of our images and then fed them to our fine-tuned pretrained model. We divided our dataset to three sets of train, validation, and test. We could achieve the accuracy of 98.6% for 50 epochs which is better than other deep-learning based methods for the same dataset in the study.
View less >
Conference Title
2021 7th International Conference on Web Research (ICWR)
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Artificial intelligence
Machine learning
Computer vision and multimedia computation