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Jul 30, 2023 · Four modules, including preprocessing and segmentation, feature extraction, CNN-BiGRU with attention classification and postprocessing, make up the structure of this automatic seizure detection system, which is shown in Fig. 1. The specifics of each section are provided in the following subsections.
May 12, 2019 · In other words, a modification in the training regime can be adopted to train a CNN-based model for feature extraction in an unsupervised manner. In an unsupervised feature-extracting CNN, the learned feature vector – and therefore also its quality with respect to the task at hand will depend on the large number of parameters contained in the network.
Jul 18, 2016 · Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of ...
Feature extraction is a critical process in computer vision, especially in Convolutional Neural Networks (CNNs). It involves identifying and isolating essential patterns and information from visual data, enabling the network to make sense of the input.
Jan 7, 2021 · Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most research, the CNN architecture for feature extraction is chosen arbitrarily, without justification. This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC). First, extensive experiments are conducted ...
Jan 5, 2023 · A CNN model has been used as a feature extractor and also as a classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified dataset where ambiguous samples have been removed.
May 1, 2024 · A convolutional neural network model named multi-level feature extraction network (MFENet) is proposed, used to extract shallow features from both digital elevation model images and multispectral images and the susceptibility index of gullies to debris flows is calculated based on the similarity scores. Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this article proposes a convolutional neural network model named ...
