Graph fourier transform based descriptor for gesture classification
01 Dec 2017-
TL;DR: Simulation results show that the proposed GFT based descriptor gives a comparable results with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors for gesture classification.
Abstract: This paper proposes a method for gesture classification based on Graph Fourier transform (GFT) coefficients. GFT coefficients are the projection of image pixel block onto the eigenvectors of a Laplacian matrix. This Laplacian matrix is generated from undirected graph, representing a spatial connectedness between each pixel within an image block. This work proposes a method for generating an undirected graph by using edge information of the image. Edge information of the image is obtained by average sum of absolute difference between the current pixel and its neighboring pixels by using an appropriate threshold. The resulting GFT based feature vector is formed by concatenating GFT coefficients of each block. The resultant feature vector is applied to linear Support Vector Machine (SVM) classifier to predict the gesture class. For NTU and Massey hand gesture datasets, threshold value 30 gives maximum prediction accuracy. We compare the results of the proposed GFT based descriptor approach with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors on three different gesture datasets: NTU, Cambridge and Massey. Simulation results show that the proposed GFT based descriptor gives a comparable results with Karhunen-Loeve transform (K-LT) and Discrete Cosine transform (DCT) based descriptors for gesture classification.
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01 Feb 2020
TL;DR: Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.
Abstract: Epileptic Seizure is a chronic nervous system disorder which is analyzed using Electroencephalogram (EEG) signals. This paper proposes a Graph Signal Processing technique called Graph Discrete Fourier Transform (GDFT) for the detection of epilepsy. EEG data points are projected on the Eigen space of Laplacian matrix of graph to produce GDFT coefficients. The Laplacian matrix is generated from weighted visibility graph constructed from EEG signals. It proposes Gaussian kernel based edge weights between the nodes. The proposed GDFT based feature vectors are then used to detect the seizure class from the given EEG signal using a crisp rule based classification. Simulation results show that the proposed GDFT based features from Gaussian Weighted Visibility Graph (VG) can detect epileptic seizure with 100 % accuracy.
15 citations
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
Additional excerpts
...LIBSVM library [25] is used for classification....
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20 Jun 2005
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Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
31,952 citations
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...We tested the proposed algorithm on the INRIA [17] dataset which is divided into pos and neg folders....
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...[17] used Histogram of Oriented Gradient (HOG) for human detection....
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14,562 citations
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...In the field of image processing, K-LT and DCT was initially used for image compression and reconstruction [26], [27] but they can also be employed for image recognition [28], [29]....
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