Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition
This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8888161/ |
| _version_ | 1819132971096997888 |
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| author | Basheer Alwaely Charith Abhayaratne |
| author_facet | Basheer Alwaely Charith Abhayaratne |
| author_sort | Basheer Alwaely |
| collection | DOAJ |
| description | This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of the eigenvectors of the normalized Laplacian of the graph adjacency matrix for forming the feature vectors. The method proposes to use the eigenvector corresponding to the lowest eigenvalue for formulating the feature vectors as it captures the details of the structure of the graph. The use of the proposed graph spectral domain representation has been demonstrated in an in-air hand-drawn number and symbol recognition applications. It has achieved average accuracy rates of 99.56% and 99.44%, for numbers and symbols, respectively, outperforming the existing methods for all datasets used. It also has the added benefits of fast real-time operation and invariance to rotation and flipping, making the recognition system robust to different writing and drawing variations. |
| first_indexed | 2024-12-22T09:39:52Z |
| format | Article |
| id | doaj.art-cbec25e610314ed9a4826aac274a7dc9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:39:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cbec25e610314ed9a4826aac274a7dc92022-12-21T18:30:44ZengIEEEIEEE Access2169-35362019-01-01715966115967310.1109/ACCESS.2019.29506438888161Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape RecognitionBasheer Alwaely0Charith Abhayaratne1https://orcid.org/0000-0002-2799-7395Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K.This paper addresses the problem of recognition of dynamic shapes by representing the structure in a shape as a graph and learning the graph spectral domain features. Our proposed method includes pre-processing for converting the dynamic shapes into a fully connected graph, followed by analysis of the eigenvectors of the normalized Laplacian of the graph adjacency matrix for forming the feature vectors. The method proposes to use the eigenvector corresponding to the lowest eigenvalue for formulating the feature vectors as it captures the details of the structure of the graph. The use of the proposed graph spectral domain representation has been demonstrated in an in-air hand-drawn number and symbol recognition applications. It has achieved average accuracy rates of 99.56% and 99.44%, for numbers and symbols, respectively, outperforming the existing methods for all datasets used. It also has the added benefits of fast real-time operation and invariance to rotation and flipping, making the recognition system robust to different writing and drawing variations.https://ieeexplore.ieee.org/document/8888161/Graph signal processinggraph spectral theoryfully connected graphsdynamic shape recognitionshape representationgraph spectral feature learning |
| spellingShingle | Basheer Alwaely Charith Abhayaratne Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition IEEE Access Graph signal processing graph spectral theory fully connected graphs dynamic shape recognition shape representation graph spectral feature learning |
| title | Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition |
| title_full | Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition |
| title_fullStr | Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition |
| title_full_unstemmed | Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition |
| title_short | Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition |
| title_sort | graph spectral domain feature learning with application to in air hand drawn number and shape recognition |
| topic | Graph signal processing graph spectral theory fully connected graphs dynamic shape recognition shape representation graph spectral feature learning |
| url | https://ieeexplore.ieee.org/document/8888161/ |
| work_keys_str_mv | AT basheeralwaely graphspectraldomainfeaturelearningwithapplicationtoinairhanddrawnnumberandshaperecognition AT charithabhayaratne graphspectraldomainfeaturelearningwithapplicationtoinairhanddrawnnumberandshaperecognition |