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ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2021 Feature ...
Matthew Henderson +5 more
openaire +2 more sources
Efficient Deep Feature Learning and Extraction via StochasticNets [PDF]
arXiv, 2015 Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data. One area worth exploring in feature learning and extraction using deep neural networks is efficient neural connectivity formation for faster feature learning and extraction.
Fieguth, Paul +3 more
arxiv +3 more sources
Journal of Applied Science and Technology Trends, 2020 Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results.
R. Zebari +4 more
semanticscholar +3 more sources
Lightweight transformer image feature extraction network [PDF]
PeerJ Computer ScienceIn recent years, the image feature extraction method based on Transformer has become a research hotspot. However, when using Transformer for image feature extraction, the model’s complexity increases quadratically with the number of tokens entered.
Wenfeng Zheng +4 more
semanticscholar +3 more sources
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification [PDF]
IEEE Transactions on Image Processing (Volume: 25, Issue: 1, Jan.
2016), 2019 The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches.
Yong Luo +4 more
arxiv +3 more sources
TSFEL: Time Series Feature Extraction Library
SoftwareX, 2020 Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scientists must consider a combination between a multitude of domain
M. Barandas +8 more
semanticscholar +3 more sources
Texture Feature Extraction Methods: A Survey
IEEE Access, 2019 Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image ...
A. Humeau-Heurtier
semanticscholar +3 more sources
Automated Feature Extraction on AsMap for Emotion Classification Using EEG [PDF]
Italian National Conference on Sensors, 2022 Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models.
Md. Zaved Iqubal Ahmed +2 more
semanticscholar +1 more source
Investigation of Biometric Identification Technology Based on biological Fingerprints and Facial Features [PDF]
E3S Web of Conferences, 2021 Biometric identification is largely dependent on feature extraction technology. As feature extraction techniques are increasingly mature, scholars have gradually turned their attention to the relevant problems between biometric characteristics.
Li Huixing, Xue Yan, Zeng Xiancai
doaj +1 more source
Survey of Deep Feature Instance Level Image Retrieval Algorithms [PDF]
Jisuanji kexue yu tansuo, 2023 Content-based image retrieval algorithm (CBIR) aims to find semantically matching or similar images with query images. It analyzes visual content in a large number of image databases. It is important to obtain discriminant image representation by feature
JI Changqing, WANG Bingbing, QIN Jing, WANG Zumin
doaj +1 more source