Improving Shape Retrieval and Classification Rates through Low-Dimensional Features Fusion

2015 ◽  
pp. 37-44
Author(s):  
Paweł Forczmański
Keyword(s):  
Shape Retrieval ◽  
Features Fusion ◽  
Low Dimensional ◽  
Low Dimensional Features

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

2013 ◽  
Vol 756-759 ◽  
pp. 4121-4125
Author(s):  
Peng Zhang ◽  
Yuan Yuan Ren
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
Target Recognition ◽  
Perceptual Information ◽  
System Control ◽  
Aerial Vehicles ◽  
Robust Feature Extraction ◽  
Image Manifold ◽  
Low Dimensional ◽  
Low Dimensional Features

Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted low-dimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.

Low dimensional features of graphene nanostructure stability and vibrational characteristics (Review)

2020 ◽  
Vol 46 (3) ◽  
pp. 232-257
Author(s):  
I. A. Gospodarev ◽  
V. A. Sirenko ◽  
E. S. Syrkin ◽  
S. B. Feodosyev ◽  
K. A. Minakova
Keyword(s):  
Vibrational Characteristics ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Graphene Nanostructure

scholarly journals Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Zhang XiuJun ◽  
Liu Chang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Factorization Algorithm ◽  
Factorization Algorithms ◽  
Low Dimensional ◽  
Low Dimensional Features

In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms.

scholarly journals Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Shingchern D. You ◽  
Ming-Jen Hung
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Temporal Data ◽  
Reduction Techniques ◽  
Temporal Features ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Motion Picture Expert Group

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

scholarly journals Combining Attention-based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection

2021 ◽  
Author(s):  
Yunan Wu ◽  
Arne Schmidt ◽  
Enrique Hernandez Sanchez ◽  
Rafael Molina ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Multiple Instance Learning ◽  
Manual Annotation ◽  
Mortality And Morbidity ◽  
Life Threatening ◽  
Low Dimensional ◽  
Level Training ◽  
Ct Slices ◽  
Timely Treatment ◽  
Low Dimensional Features ◽  
Attention Weight

Intracranial hemorrhage (ICH) is a life-threatening emergency with high rates of mortality and morbidity. Rapid and accurate detection of ICH is crucial for patients to get a timely treatment. In order to achieve the automatic diagnosis of ICH, most deep learning models rely on huge amounts of slice labels for training. Unfortunately, the manual annotation of CT slices by radiologists is time-consuming and costly. To diagnose ICH, in this work, we propose to use an attention-based multiple instance learning (Att-MIL) approach implemented through the combination of an attention-based convolutional neural network (Att-CNN) and a variational Gaussian process for multiple instance learning (VGPMIL). Only labels at scan-level are necessary for training. Our method (a) trains the model using scan labels and assigns each slice with an attention weight, which can be used to provide slice-level predictions, and (b) uses the VGPMIL model based on low-dimensional features extracted by the Att-CNN to obtain improved predictions both at slice and scan levels. To analyze the performance of the proposed approach, our model has been trained on 1150 scans from an RSNA dataset and evaluated on 490 scans from an external CQ500 dataset. Our method outperforms other methods using the same scan-level training and is able to achieve comparable or even better results than other methods relying on slice-level annotations.

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