Indexing of Image Features Using Quadtree

2016 ◽  
pp. 185-203
Author(s):  
N. Puviarasan ◽  
R. Bhavani
Keyword(s):  
Extraction Method ◽  
Dimensional Space ◽  
Texture Features ◽  
Image Features ◽  
High Dimensional ◽  
Shape Features ◽  
Indexing Methods ◽  
Feature Extraction Method ◽  
Indexing Method ◽  
Color Texture

In Content based image retrieval (CBIR) applications, the idea of indexing is mapping the extracted descriptors from images into a high-dimensional space. In this paper, visual features like color, texture and shape are considered. The color features are extracted using color coherence vector (CCV), texture features are obtained from Segmentation based Fractal Texture Analysis (SFTA). The shape features of an image are extracted using the Fourier Descriptors (FD) which is the contour based feature extraction method. All features of an image are then combined. After combining the color, texture and shape features using appropriate weights, the quadtree is used for indexing the images. It is experimentally found that the proposed indexing method using quadtree gives better performance than the other existing indexing methods.

scholarly journals An Incremental Version of L-MVU for the Feature Extraction of MI-EEG

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Mingai Li ◽  
Hongwei Xi ◽  
Xiaoqing Zhu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Average Energy ◽  
High Dimensional ◽  
Dimensional Representation ◽  
Feature Extraction Method ◽  
Sample Data ◽  
Out Of Sample ◽  
Nonlinear Features ◽  
Low Dimensional

Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy. As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG. However, L-MVU still requires considerable computation costs for out-of-sample data. An incremental version of L-MVU (denoted as IL-MVU) is proposed in this paper. The low-dimensional representation of the training data is generated by L-MVU. For each out-of-sample data, its nearest neighbors will be found in the high-dimensional training samples and the corresponding reconstruction weight matrix be calculated to generate its low-dimensional representation as well. IL-MVU is further combined with the dual-tree complex wavelet transform (DTCWT), which develops a hybrid feature extraction method (named as IL-MD). IL-MVU is applied to extract the nonlinear features of the specific subband signals, which are reconstructed by DTCWT and have the obvious event-related synchronization/event-related desynchronization phenomenon. The average energy features of α and β waves are calculated simultaneously. The two types of features are fused and are evaluated by a linear discriminant analysis classifier. Based on the two public datasets with 12 subjects, extensive experiments were conducted. The average recognition accuracies of 10-fold cross-validation are 92.50% on Dataset 3b and 88.13% on Dataset 2b, and they gain at least 1.43% and 3.45% improvement, respectively, compared to existing methods. The experimental results show that IL-MD can extract more accurate features with relatively lower consumption cost, and it also has better feature visualization and self-adaptive characteristics to subjects. The t-test results and Kappa values suggest the proposed feature extraction method reaches statistical significance and has high consistency in classification.

Research of Feature Extraction Method on Facial Expression Change

2011 ◽  
Vol 211-212 ◽  
pp. 813-817 ◽  
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Facial Expression ◽  
Extraction Method ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Low Frequency ◽  
Expression Change ◽  
Feature Extraction Method ◽  
Low Dimensional

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

scholarly journals A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Yuntao Zhao ◽  
Bo Bo ◽  
Yongxin Feng ◽  
ChunYu Xu ◽  
Bo Yu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Dimensional Space ◽  
Semantic Segmentation ◽  
Dynamic Feature ◽  
Feature Extraction Method ◽  
Malicious Behavior ◽  
Internet Users ◽  
Performance Indexes ◽  
N Gram

With explosive growth of malware, Internet users face enormous threats from Cyberspace, known as “fifth dimensional space.” Meanwhile, the continuous sophisticated metamorphism of malware such as polymorphism and obfuscation makes it more difficult to detect malicious behavior. In the paper, based on the dynamic feature analysis of malware, a novel feature extraction method of hybrid gram (H-gram) with cross entropy of continuous overlapping subsequences is proposed, which implements semantic segmentation of a sequence of API calls or instructions. The experimental results show the H-gram method can distinguish malicious behaviors and is more effective than the fixed-length n-gram in all four performance indexes of the classification algorithms such as ID3, Random Forest, AdboostM1, and Bagging.

scholarly journals Difference Curvature Multidimensional Network for Hyperspectral Image Super-Resolution

2021 ◽  
Vol 13 (17) ◽  
pp. 3455
Author(s):  
Chi Zhang ◽  
Mingjin Zhang ◽  
Yunsong Li ◽  
Xinbo Gao ◽  
Shi Qiu
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Space ◽  
Super Resolution ◽  
Texture Features ◽  
High Dimensional ◽  
Great Success ◽  
Spectral Correlation ◽  
High Dimensional Space ◽  
Image Super Resolution ◽  
Difference Curvature

In recent years, convolutional-neural-network-based methods have been introduced to the field of hyperspectral image super-resolution following their great success in the field of RGB image super-resolution. However, hyperspectral images appear different from RGB images in that they have high dimensionality, implying a redundancy in the high-dimensional space. Existing approaches struggle in learning the spectral correlation and spatial priors, leading to inferior performance. In this paper, we present a difference curvature multidimensional network for hyperspectral image super-resolution that exploits the spectral correlation to help improve the spatial resolution. Specifically, we introduce a multidimensional enhanced convolution (MEC) unit into the network to learn the spectral correlation through a self-attention mechanism. Meanwhile, it reduces the redundancy in the spectral dimension via a bottleneck projection to condense useful spectral features and reduce computations. To remove the unrelated information in high-dimensional space and extract the delicate texture features of a hyperspectral image, we design an additional difference curvature branch (DCB), which works as an edge indicator to fully preserve the texture information and eliminate the unwanted noise. Experiments on three publicly available datasets demonstrate that the proposed method can recover sharper images with minimal spectral distortion compared to state-of-the-art methods. PSNR/SAM is 0.3–0.5 dB/0.2–0.4 better than the second best methods.

scholarly journals Tracking and Extracting Action Trajectory of Athlete Based on Hierarchical Features

2020 ◽  
Vol 25 (5) ◽  
pp. 677-682
Author(s):  
Tao Pan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Poor Performance ◽  
Feature Space ◽  
Image Features ◽  
Feature Extraction Method ◽  
Feature Sets ◽  
Simulation Results ◽  
Hierarchical Features ◽  
Different Levels

The feature extraction from athlete action images is a research hotspot. To accurately evaluate athlete actions, it is necessary to partition the original image into refined blocks, and extract different levels of image features. However, the traditional feature extraction algorithms can only roughly divide action images into several classes, failing to acquire the accurate feature sets of the actions. This leads to relatively poor performance of feature extraction from action images. To overcome the defect of the traditional methods, this paper puts forward a feature extraction method for the action images of badminton players based on hierarchical features. The underlying image features were analyzed based on the techniques of badminton players, and mapped to the feature space of the corresponding dimension. Simulation results show that the proposed method can accurately extract the features from athlete action images.

scholarly journals An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Sun-Young Ihm ◽  
Jae-Hee Hur ◽  
Young-Ho Park
Keyword(s):  
Convex Hull ◽  
Query Processing ◽  
Input Data ◽  
Large Data ◽  
High Dimensional ◽  
Mobile Environments ◽  
Indexing Methods ◽  
Indexing Method ◽  
Index Building ◽  
Efficient Query Processing

A top-k query processing is widely used in many applications and mobile environments. An index is used for efficient query processing and layer-based indexing methods are representative to perform the top-k query processing efficiently. However, the existing methods have a problem of high index building time for multidimensional and large data; thus, it is difficult to use them. In this paper, we proposed a new concept of constructing layer-based index, which is called unbalanced layer (UB-Layer). The existing methods construct a layer as a balanced layer with outermost data and wrap the rest of the input data. However, UB-Layer constructs a layer as an unbalanced layer that does not wrap the rest of the data. To construct UB-Layer, we fist divide the dimension of the input data into divided-dimensional data and compute the convex hull in each divided-dimensional data. And then, we combine divided-convex hull to build UB-Layer. We also propose UB-SelectAttribute algorithm for dividing the dimension with major attributes. We demonstrate the superiority of the proposed methods by the performance experiments.

Research on Feature Extraction of Wheat Leaf Disease Image

2011 ◽  
Vol 317-319 ◽  
pp. 1326-1329
Author(s):  
Zhi Hua Diao ◽  
Yuan Yuan Wu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Texture Features ◽  
Shape Features ◽  
Color Spaces ◽  
Feature Extraction Method ◽  
Wheat Leaf ◽  
Occurrence Matrix ◽  
Application Requirements ◽  
Intelligent Recognition

In order to resolve the problem of not taking into account color, texture and shape features in crop disease intelligent recognition systems, feature extraction method based on three feature types was proposed. Two types of color spaces such as RGB and HIS were considered, and the transformation formula of the two color spaces was improved. Then ten color features were defined and extracted. Meanwhile sixteen texture features were defined and extracted based on gray level co-occurrence matrix. And thirteen shape features were defined and extracted based on invariant moment theory. Finally the feature dataset was received which was suitable for identifying four types of wheat leaf diseases. The results show that the system recognition rate is relatively high, and can meet the practical application requirements.

Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2558-2565
Author(s):  
K. Kavinkumar ◽  
T. Meeradevi
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector Machine Classifier ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction

Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classification is done by training SVM using the texture features from the hybrid and efficient feature extraction technique.The accuracy of the classification is calculated and the SVM classifier training individual type of texture features and also with combined texture features and the performance is analyzed.

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