Tackling class imbalance in computer vision: a contemporary review
Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …
such as medical image analysis, objection detection and recognition, image segmentation …
An overview of cluster-based image search result organization: background, techniques, and ongoing challenges
J Tekli - Knowledge and Information Systems, 2022 - Springer
Digital photographs and visual data have become increasingly available, especially on the
Web considered as the largest image database to date. However, the value of multimedia …
Web considered as the largest image database to date. However, the value of multimedia …
Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets
Screening and diagnosis of diabetic retinopathy disease is a well known problem in the
biomedical domain. The use of medical imagery from a patient's eye for detecting the …
biomedical domain. The use of medical imagery from a patient's eye for detecting the …
Vggin-net: Deep transfer network for imbalanced breast cancer dataset
In this paper, we have presented a novel deep neural network architecture involving transfer
learning approach, formed by freezing and concatenating all the layers till block4 pool layer …
learning approach, formed by freezing and concatenating all the layers till block4 pool layer …
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset
D Katare, DS Noguero, S Park, N Kourtellis… - arXiv preprint arXiv …, 2024 - arxiv.org
The accuracy and fairness of perception systems in autonomous driving are crucial,
particularly for vulnerable road users. Mainstream research has looked into improving the …
particularly for vulnerable road users. Mainstream research has looked into improving the …
Improving bag-of-deep-visual-words model via combining deep features with feature difference vectors
X Wang - IEEE Access, 2022 - ieeexplore.ieee.org
Bag-of-Deep-Visual-Words (BoDVW) model has shown its advantage over Convolutional
Neural Network (CNN) model in image classification tasks with a small number of training …
Neural Network (CNN) model in image classification tasks with a small number of training …
Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?
Y Xu, X Zhang, C Huang, X Qiu - Plos one, 2024 - journals.plos.org
This article investigates whether higher classification accuracy can always be achieved by
utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep …
utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep …
Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features
Accurate identification of individual tree species (ITS) is crucial to forest management.
However, current ITS identification methods are mainly based on traditional image features …
However, current ITS identification methods are mainly based on traditional image features …
Cervical cancer screening on multi-class imbalanced cervigram dataset using transfer learning
Image classification from a multi-class imbalanced dataset is challenging because it is
difficult to detect all the minority classes present in the datasets. In this paper, the authors …
difficult to detect all the minority classes present in the datasets. In this paper, the authors …
New indicators and standards for measuring of the end mill's helical groove by image processing
The studies will be carried out using optical metrology methods on a Walter Helicheck
inspection machine in reflected light and a number of images were stored to form a statistical …
inspection machine in reflected light and a number of images were stored to form a statistical …