Fine-grained image classification using deep learning

Fine-grained image categorization, also known as sub-category recognition, is a popular research topic in computer vision and pattern recognition in recent years. The goal of this task is to classify images belonging to the same basic category (e.g., car, dog, flower, bird, etc.) into more detailed...

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Main Author: Sun, Deguang
Other Authors: Yap Kim Hui
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/154664
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1546642023-07-04T16:39:17Z Fine-grained image classification using deep learning Sun, Deguang Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Fine-grained image categorization, also known as sub-category recognition, is a popular research topic in computer vision and pattern recognition in recent years. The goal of this task is to classify images belonging to the same basic category (e.g., car, dog, flower, bird, etc.) into more detailed sub-categories. Compared with ordinary image classification tasks, fine-grained image categorization is a more challenging task due to the subtle inter-class differences and large intra-class variations between sub-categories. In order to successfully classify two very similar species at a fine-grained level, it is most important to find the discriminative part in the images that can distinguish the two species, and to be able to represent the characteristics of these discriminative parts well. Most of existing methods for fine-grained classification of images are based on deep convolutional networks to learn a robust representation of image features. However, it is difficult to accomplish fine-grained image classification tasks with high accuracy by relying on a backbone neural network alone. This dissertation aims to evaluate the performance of two different network-based fine-grained classification models, i.e., Progressive Multi-Granularity training framework and A Transformer Architecture for Fine-grained Recognition, on a wide range of the special-purpose fine-grained classification datasets. Both methods are currently state-of-the-art. The detailed and comprehensive experiments and in-depth analysis of the results provide valuable insight for future study on fine-grained image classification. Master of Science (Signal Processing) 2022-01-03T07:53:13Z 2022-01-03T07:53:13Z 2021 Thesis-Master by Coursework Sun, D. (2021). Fine-grained image classification using deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154664 https://hdl.handle.net/10356/154664 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Sun, Deguang
Fine-grained image classification using deep learning
description Fine-grained image categorization, also known as sub-category recognition, is a popular research topic in computer vision and pattern recognition in recent years. The goal of this task is to classify images belonging to the same basic category (e.g., car, dog, flower, bird, etc.) into more detailed sub-categories. Compared with ordinary image classification tasks, fine-grained image categorization is a more challenging task due to the subtle inter-class differences and large intra-class variations between sub-categories. In order to successfully classify two very similar species at a fine-grained level, it is most important to find the discriminative part in the images that can distinguish the two species, and to be able to represent the characteristics of these discriminative parts well. Most of existing methods for fine-grained classification of images are based on deep convolutional networks to learn a robust representation of image features. However, it is difficult to accomplish fine-grained image classification tasks with high accuracy by relying on a backbone neural network alone. This dissertation aims to evaluate the performance of two different network-based fine-grained classification models, i.e., Progressive Multi-Granularity training framework and A Transformer Architecture for Fine-grained Recognition, on a wide range of the special-purpose fine-grained classification datasets. Both methods are currently state-of-the-art. The detailed and comprehensive experiments and in-depth analysis of the results provide valuable insight for future study on fine-grained image classification.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Sun, Deguang
format Thesis-Master by Coursework
author Sun, Deguang
author_sort Sun, Deguang
title Fine-grained image classification using deep learning
title_short Fine-grained image classification using deep learning
title_full Fine-grained image classification using deep learning
title_fullStr Fine-grained image classification using deep learning
title_full_unstemmed Fine-grained image classification using deep learning
title_sort fine-grained image classification using deep learning
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/154664
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