scholarly journals Extracting Multiple Features for Dynamic Hand Gesture Recognition

2021 ◽  
Vol 10 (4) ◽  
pp. 71-75
Author(s):  
Suni S S ◽  
Gopakumar K
Keyword(s):  
Support Vector ◽  
Hand Gestures ◽  
Local Descriptor ◽  
Multiple Features ◽  
Local Shape ◽  
Machine Performance ◽  
Motion Descriptor ◽  
Dynamic Hand Gesture Recognition ◽  
Global And Local ◽  
New Framework

In this work a framework based on histogram of orientation of optical flow (HOOF) and local binary pattern from three orthogonal planes (LBP_TOP) is proposed for recognizing dynamic hand gestures. HOOF algorithm extracts local shape and dynamic motion information of gestures from image sequences and local descriptor LBP is extended to three orthogonal planes to create an efficient motion descriptor. These features are invariant to scale, translation, illumination and direction of motion. The performance of the new framework is tested in two different ways. The first one is by fusing the global and local features as one descriptor and the other is using features separately to train the multi class support vector machine. Performance analysis shows that the proposed approach produces better results for recognizing dynamic hand gestures when compared with state of the art methods

scholarly journals GLOBAL AND LOCAL DESCRIPTOR FOR CBIR AND IMAGE ENHANCEMENT USING MULTI-FEATURE FUSION METHOD

2016 ◽  
Vol 4 (6) ◽  
pp. 170-182 ◽  
Author(s):  
Devrat Arya ◽  
Jaimala Jha
Keyword(s):  
Feature Fusion ◽  
Experimental Result ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Local Descriptor ◽  
Correlation Vector ◽  
Main Work ◽  
Color Structure ◽  
Global Correlation ◽  
Global And Local

The research is ongoing in CBIR it is getting much popular. In this retrieval of image is done using a technique that searches the necessary features of image. The main work of CBIR is to get retrieve efficient, perfect and fast results.In this algorithm, fused multi-feature for color, texture and figure features. A global and local descriptor (GLD) is proposed in this paper, called Global Correlation Descriptor (GCD) and Discrete Wavelet Transform (DWT), to excerpt color and surface feature respectively so that these features have the same effect in CBIR. In addition, Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) is proposed in this paper which can integrate the advantages of histogram statistics and Color Structure Descriptor (CSD) to characterize color and consistency features respectively. Also, this paper is implemented by Hu moment (HM) for shape feature, it extract 8 moments for image. For the classification process, apply kernel Support vector machine (SVM). The experimental result has computed precision, recall, f_measure and execution time. Also, worked on two datasets: Corel-1000 and Soccer-280.

Monocular face reconstruction with global and local shape constraints

2015 ◽  
Vol 149 ◽  
pp. 1535-1543 ◽  
Author(s):  
Jian Zhang ◽  
Dapeng Tao ◽  
Xiangjuan Bian ◽  
Xiaosi Zhan
Keyword(s):  
Local Shape ◽  
Shape Constraints ◽  
Face Reconstruction ◽  
Global And Local

scholarly journals Recognition of Human Emotion Detection and Annotation, using Local Descriptor and Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 2838-2843
Keyword(s):  
Support Vector Machine ◽  
Object Detection ◽  
Dynamic Environment ◽  
Detection Algorithm ◽  
Research Area ◽  
Multimedia Data ◽  
Video Annotation ◽  
Support Vector ◽  
People Detection ◽  
Local Descriptor

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

Age Classification Based on Feature Fusion

2014 ◽  
Vol 519-520 ◽  
pp. 644-650
Author(s):  
Mian Shui Yu ◽  
Yu Xie ◽  
Xiao Meng Xie
Keyword(s):  
Feature Fusion ◽  
Principal Component ◽  
Directional Pattern ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Classification Problems ◽  
Pca Method ◽  
Multi Class Classification ◽  
Global And Local ◽  
Fusion Theory

Age classification based on facial images is attracting wide attention with its broad application to human-computer interaction (HCI). Since human senescence is a tremendously complex process, age classification is still a highly challenging issue. In our study, Local Directional Pattern (LDP) and Gabor wavelet transform were used to extract global and local facial features, respectively, that were fused based on information fusion theory. The Principal Component Analysis (PCA) method was used for dimensionality reduction of the fused features, to obtain a lower-dimensional age characteristic vector. A Support Vector Machine (SVM) multi-class classifier with Error Correcting Output Codes (ECOC) was proposed in the paper. This was aimed at multi-class classification problems, such as age classification. Experiments on a public FG-NET age database proved the efficiency of our method.

scholarly journals Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

2019 ◽  
Vol 9 (15) ◽  
pp. 3130 ◽  
Author(s):  
Navarro ◽  
Perez
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
Texture Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Post Processing ◽  
Image Partition ◽  
Global And Local ◽  
Bagging Ensemble ◽  
Color Texture

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

scholarly journals A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guoliang Chen ◽  
Kaikai Ge
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Calculation Model ◽  
Opening Angle ◽  
Hand Gesture ◽  
Leap Motion ◽  
Recognition Method ◽  
Hand Gestures ◽  
Multiple Features

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

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