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Object‐aware deep feature extraction for feature matching
Concurrency and Computation: Practice and Experience, 2023SummaryFeature extraction is a fundamental step in the feature matching task. A lot of studies are devoted to feature extraction. Recent researches propose to extract features by pre‐trained neural networks, and the output is used for feature matching.
Zuoyong Li +4 more
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Learning deep classifiers with deep features
2016 IEEE International Conference on Multimedia and Expo (ICME), 2016Visual separability between different objects in various image classification tasks is highly uneven. As a consequence, humans need different levels of detailed descriptions to separate objects in multi-granularity similarities. Meanwhile, deep networks, such as convolutional neural networks (C-NNs) have demonstrated great ability in multilevel ...
Jie Lei 0002 +5 more
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Supervised Deep Feature Embedding With Handcrafted Feature
IEEE Transactions on Image Processing, 2019Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for ...
Shichao Kan +5 more
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Speaker verification with deep features
2014 International Joint Conference on Neural Networks (IJCNN), 2014Due to great success of deep learning in speech recognition, there has been interest of applying deep learning to speaker verification. Previous investigations usually focus on using deep neural network as new classifiers or to extract speaker dependent features.
Yuan Liu +4 more
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2018
Feature extraction is the first step in building real-life classification engines—it aims at elaborating features to characterize objects that are to be labeled by a trained model. Time-consuming feature extraction requires domain expertise to effectively design features.
Jakub Nalepa +2 more
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Feature extraction is the first step in building real-life classification engines—it aims at elaborating features to characterize objects that are to be labeled by a trained model. Time-consuming feature extraction requires domain expertise to effectively design features.
Jakub Nalepa +2 more
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Deep Semantic Feature Matching
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017Estimating dense visual correspondences between objects with intra-class variation, deformations and background clutter remains a challenging problem. Thanks to the breakthrough of CNNs there are new powerful features available. Despite their easy accessibility and great success, existing semantic flow methods could not significantly benefit from these
Nikolai Ufer, Björn Ommer
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Combining Deep Feature and Handcrafted Features for Material Classification
2018 10th International Conference on Knowledge and Systems Engineering (KSE), 2018Material classification is a challenging problem in robot and computer vision. The deep learning methods have achieved major success in object classification, but they do not conquer in material classification. One of the main reasons is different materials may yield very similar appearance. In this paper, we propose a new method combining deep feature
Truong Phuc Anh, Tien-Dung Mai
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Feature Extraction of ECG Signal by using Deep Feature
2019 7th International Symposium on Digital Forensics and Security (ISDFS), 2019The analysis and classification of Electrocardiogram (ECG) signals have become very important tool to diagnose of heart disorders. Computer-aided techniques are generally used to classify biomedical application areas. In this paper, we aim to feature extraction and classification of ECG signals. Accordingly, an open access ECG database in Physionet was
Aykut Diker, Engin Avci
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Deep Graphical Feature Learning for the Feature Matching Problem
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019The feature matching problem is a fundamental problem in various areas of computer vision including image registration, tracking and motion analysis. Rich local representation is a key part of efficient feature matching methods. However, when the local features are limited to the coordinate of key points, it becomes challenging to extract rich local ...
Zhen Zhang 0008, Wee Sun Lee
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2018
FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
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FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
openaire +1 more source