Feature Extraction Algorithm Based on Sample Set Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2241-2245
Author(s):  
Xiao Yuan Jing ◽  
Xiang Long Ge ◽  
Yong Fang Yao ◽  
Feng Nan Yu
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Training Sample ◽  
Classification Performance ◽  
Related Information ◽  
Training Samples ◽  
Extraction Algorithm ◽  
The Difference ◽  
Sample Set ◽  
Traditional Image

When the number of labeled training samples is very small, the sample information people can use would be very little and the recognition rates of traditional image recognition methods are not satisfactory. However, there is often some related information contained in other databases that is helpful to feature extraction. Thus, it is considered to take full advantage of the data information in other databases by transfer learning. In this paper, the idea of transferring the samples is employed and further we propose a feature extraction approach based on sample set reconstruction. We realize the approach by reconstructing the training sample set using the difference information among the samples of other databases. Experimental results on three widely used face databases AR, FERET, CAS-PEAL are presented to demonstrate the efficacy of the proposed approach in classification performance.

Research on Image Feature Extraction Method Based on Orthogonal Projection Transformation of Multi-Task Learning Technology

2013 ◽  
Vol 760-762 ◽  
pp. 1609-1614
Author(s):  
Xiao Yuan Jing ◽  
Li Li ◽  
Cai Ling Wang ◽  
Yong Fang Yao ◽  
Feng Nan Yu
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Classification Performance ◽  
Image Feature ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Task Learning ◽  
Related Information ◽  
Training Samples ◽  
Projection Transformation

When the number of labeled training samples is very small, the sample information we can use would be very little. Because of this, the recognition rates of some traditional image recognition methods are not satisfactory. In order to use some related information that always exist in other databases, which is helpful to feature extraction and can improve the recognition rates, we apply multi-task learning to feature extraction of images. Our researches are based on transferring the projection transformation. Our experiments results on the public AR, FERET and CAS-PEAL databases demonstrate that the proposed approaches are more effective than the general related feature extraction methods in classification performance.

EVALUATE DISSIMILARITY OF SAMPLES IN FEATURE SPACE FOR IMPROVING KPCA

2011 ◽  
Vol 10 (03) ◽  
pp. 479-495 ◽  
Author(s):  
XU YONG ◽  
DAVID ZHANG ◽  
JIAN YANG ◽  
JIN ZHONG ◽  
JINGYU YANG
Keyword(s):  
Feature Extraction ◽  
Linear Combination ◽  
Feature Space ◽  
Training Sample ◽  
Maximum Amount ◽  
Experimental Result ◽  
Feature Extraction Method ◽  
High Classification Accuracy ◽  
Training Samples ◽  
Sample Set

Since in the feature space the eigenvector is a linear combination of all the samples from the training sample set, the computational efficiency of KPCA-based feature extraction falls as the training sample set grows. In this paper, we propose a novel KPCA-based feature extraction method that assumes that an eigenvector can be expressed approximately as a linear combination of a subset of the training sample set ("nodes"). The new method selects maximally dissimilar samples as nodes. This allows the eigenvector to contain the maximum amount of information of the training sample set. By using the distance metric of training samples in the feature space to evaluate their dissimilarity, we devised a very simple and quite efficient algorithm to identify the nodes and to produce the sparse KPCA. The experimental result shows that the proposed method also obtains a high classification accuracy.

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

scholarly journals Urban Land-Cover Classification Using Side-View Information from Oblique Images

2020 ◽  
Vol 12 (3) ◽  
pp. 390
Author(s):  
Changlin Xiao ◽  
Rongjun Qin ◽  
Xiao Ling
Keyword(s):  
Feature Extraction ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Side View ◽  
High Resolution Data ◽  
Training Samples ◽  
Surface Models ◽  
Extraction Algorithm ◽  
Oblique Images

Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features.

Research on the Harris Algorithm of Feature Extraction for Moving Targets in the Video

2015 ◽  
Vol 741 ◽  
pp. 378-381
Author(s):  
Jun Yan ◽  
Yan Piao
Keyword(s):  
Feature Extraction ◽  
Video Sequence ◽  
Threshold Value ◽  
Moving Targets ◽  
Rapid Acquisition ◽  
Central Pixel ◽  
Extraction Algorithm ◽  
Detection Window ◽  
Feature Information ◽  
The Difference

This paper studies a problem which is the rapid acquisition feature information of moving targets in a video sequence. Selecting the harris algorithm as a feature extraction algorithm, but harris algorithm has a problem of running slowly .Opting 3 * 3 window as the detection window, analyzing the similarity between the window capacity pixel and central pixel, then using the difference to measure this similarity of pixels, when the difference is greater than the set threshold value, we thought they are different, otherwise they are similar.After that characteristics of regional screening was completed. The target areas were divided into feature areas and non-feature regions, but we only calculated the response functions of characteristic areas. The algorithm’s executing efficiency improved.

scholarly journals 3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng Di ◽  
Jing Peng ◽  
Yihua Di ◽  
Siwei Wu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Qr Decomposition ◽  
Small Sample ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
The Difference ◽  
Difference Criterion

Through the analysis of facial feature extraction technology, this paper designs a lightweight convolutional neural network (LW-CNN). The LW-CNN model adopts a separable convolution structure, which can propose more accurate features with fewer parameters and can extract 3D feature points of a human face. In order to enhance the accuracy of feature extraction, a face detection method based on the inverted triangle structure is used to detect the face frame of the images in the training set before the model extracts the features. Aiming at the problem that the feature extraction algorithm based on the difference criterion cannot effectively extract the discriminative information, the Generalized Multiple Maximum Dispersion Difference Criterion (GMMSD) and the corresponding feature extraction algorithm are proposed. The algorithm uses the difference criterion instead of the entropy criterion to avoid the “small sample” problem, and the use of QR decomposition can extract more effective discriminative features for facial recognition, while also reducing the computational complexity of feature extraction. Compared with traditional feature extraction methods, GMMSD avoids the problem of “small samples” and does not require preprocessing steps on the samples; it uses QR decomposition to extract features from the original samples and retains the distribution characteristics of the original samples. According to different change matrices, GMMSD can evolve into different feature extraction algorithms, which shows the generalized characteristics of GMMSD. Experiments show that GMMSD can effectively extract facial identification features and improve the accuracy of facial recognition.

scholarly journals A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Chunlin Gong ◽  
Liangxian Gu
Keyword(s):  
Learning Algorithm ◽  
Sampling Strategy ◽  
Training Sample ◽  
Classification Performance ◽  
Online Data ◽  
Classification Approach ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Efficient Learning ◽  
Sample Set

In many practical engineering applications, data are usually collected in online pattern. However, if the classes of these data are severely imbalanced, the classification performance will be restricted. In this paper, a novel classification approach is proposed to solve the online data imbalance problem by integrating a fast and efficient learning algorithm, that is, Extreme Learning Machine (ELM), and a typical sampling strategy, that is, the synthetic minority oversampling technique (SMOTE). To reduce the severe imbalance, the granulation division for major-class samples is made according to the samples’ distribution characteristic, and the original samples are replaced by the obtained granule core to prepare a balanced sample set. In online stage, we firstly make granulation division for minor-class and then conduct oversampling using SMOTE in the region around granule core and granule border. Therefore, the training sample set is gradually balanced and the online ELM model is dynamically updated. We also theoretically introduce fuzzy information entropy to prove that the proposed approach has the lower bound of model reliability after undersampling. Numerical experiments are conducted on two different kinds of datasets, and the results demonstrate that the proposed approach outperforms some state-of-the-art methods in terms of the generalization performance and numerical stability.

scholarly journals Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples

2021 ◽  
Vol 13 (3) ◽  
pp. 34-46
Author(s):  
Shiqi Wu ◽  
Bo Wang ◽  
Jianxiang Zhao ◽  
Mengnan Zhao ◽  
Kun Zhong ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Source Identification ◽  
Diffusion Theory ◽  
Training Sample ◽  
Virtual Sample ◽  
Image Source ◽  
Camera Identification ◽  
Average Accuracy ◽  
Training Samples ◽  
Sample Set

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.

DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION

2003 ◽  
Vol 03 (03) ◽  
pp. 503-521
Author(s):  
JUN SUN ◽  
WENYUAN WANG ◽  
QING ZHUO ◽  
CHENGYUAN MA
Keyword(s):  
Feature Extraction ◽  
Sparse Coding ◽  
Recognition Performance ◽  
Classification Performance ◽  
Image Features ◽  
Face Image ◽  
Image Feature ◽  
Training Samples ◽  
The Subject ◽  
Independent Features

Feature extraction is very important in the subject of pattern recognition. Sparse coding is an approach for extracting the independent features of an image. The image features extracted by sparse coding have led to better recognition performance as compared to those from traditional PCA-based methods. A new discriminatory sparse coding (DSC) algorithm is proposed in this paper to further improve the classification performance. Based on reinforcement learning, DSC encodes the training samples by individual class rather than by individual image as in sparse coding. Having done that it will produce a set of features with large and small intraclass variations, which is very suitable for recognition tasks. Experiments are performed on face image feature extraction and recognition. Compared with the traditional PCA- and ICA-based methods, DSC shows a much better recognition performance.

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