Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication Year
- Publication
- Publication Type
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
Computer Science Faculty Publications
Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Turkish Journal of Electrical Engineering and Computer Sciences
Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …
Providing A Framework For Seagrass Mapping In United States Coastal Ecosystems Using High Spatial Resolution Satellite Imagery, Megan M. Coffer, David D. Graybill, Peter J. Whitman, Blake A. Schaeffer, Wilson B. Salls, Richard C. Zimmerman, Victoria Hill, Marie Cindy Lebrasse, Jiang Li, Darryl J. Keith, James Kaldy, Phil Colarusso, Gary Raulerson, David Ward, W. Judson Kenworthy
Providing A Framework For Seagrass Mapping In United States Coastal Ecosystems Using High Spatial Resolution Satellite Imagery, Megan M. Coffer, David D. Graybill, Peter J. Whitman, Blake A. Schaeffer, Wilson B. Salls, Richard C. Zimmerman, Victoria Hill, Marie Cindy Lebrasse, Jiang Li, Darryl J. Keith, James Kaldy, Phil Colarusso, Gary Raulerson, David Ward, W. Judson Kenworthy
OES Faculty Publications
Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no …
Lvq Treatment For Zero-Shot Learning, Firat İsmai̇loğlu
Lvq Treatment For Zero-Shot Learning, Firat İsmai̇loğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically attempts to learn a mapping from the (visual) feature space to the semantic space in which the classes are represented by a list of semantically meaningful attributes. However, the fact that this mapping is learned without using instances of the test classes affects the performance of ZSL, which is known as the domain shift problem. In this study, we propose to apply the learning vector quantization (LVQ) algorithm …
Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin
Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …
Performance Across Worldview-2 And Rapideye For Reproducible Seagrass Mapping, Megan M. Coffer, Blake A. Schaeffer, Richard C. Zimmerman, Victoria Hill, Jiang Li, Kazi A. Islam, Peter J. Whitman
Performance Across Worldview-2 And Rapideye For Reproducible Seagrass Mapping, Megan M. Coffer, Blake A. Schaeffer, Richard C. Zimmerman, Victoria Hill, Jiang Li, Kazi A. Islam, Peter J. Whitman
OES Faculty Publications
Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe's WorldView-2 and Planet's RapidEye. A single scene from each platform …
Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin
Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …
Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li
Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li
Electrical & Computer Engineering Faculty Publications
We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear …
Detection Of Seagrass Scars Using Sparse Coding And Morphological Filter, Ender Oguslu, Sertan Erkanli, Victoria J. Hill, W. Paul Bissett, Richard C. Zimmerman, Jiang Li, Charles R. Bostater Jr. (Ed.), Stelios P. Mertikas (Ed.), Xavier Neyt (Ed.)
Detection Of Seagrass Scars Using Sparse Coding And Morphological Filter, Ender Oguslu, Sertan Erkanli, Victoria J. Hill, W. Paul Bissett, Richard C. Zimmerman, Jiang Li, Charles R. Bostater Jr. (Ed.), Stelios P. Mertikas (Ed.), Xavier Neyt (Ed.)
OES Faculty Publications
We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final …
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)
Electrical & Computer Engineering Faculty Publications
We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l1/lq regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data …
Steganalysis Feature Improvement Using Expectation Maximization, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Steganalysis Feature Improvement Using Expectation Maximization, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Faculty Publications
No abstract provided.
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Faculty Publications
No abstract provided.