DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset
Sign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set rep...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-08-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2017.0394 |
| _version_ | 1797684831359860736 |
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| author | Garima Joshi Renu Vig Sukhwinder Singh |
| author_facet | Garima Joshi Renu Vig Sukhwinder Singh |
| author_sort | Garima Joshi |
| collection | DOAJ |
| description | Sign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set represents different shape information. A simple concatenation results in large feature vector size and increases the classification computational complexity. Discriminant correlation analysis (DCA)‐based unimodal feature‐level fusion has been applied on uniform as well as complex background Indian sign language datasets. DCA is a feature‐level fusion technique that takes into account the class associations while combining the feature sets. It maximises the inter‐class separability of two feature sets and also minimises the intra‐class separability while performing the feature fusion. The objective of DCA‐based unimodal feature fusion technique is to combine different feature sets into a single feature vector with more discriminative power. The performance of proposed framework is compared with individual orthogonal moment‐based feature sets and canonical correlation analysis (CCA)‐based feature fusion technique. Results show that in comparison to individual features and CCA‐based fused features, DCA is an effective technique in terms of improved accuracy, reduced feature vector size and smaller classification time. |
| first_indexed | 2024-03-12T00:35:26Z |
| format | Article |
| id | doaj.art-b3811ab19bf44a93beda139855b58687 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:26Z |
| publishDate | 2018-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-b3811ab19bf44a93beda139855b586872023-09-15T09:48:11ZengWileyIET Computer Vision1751-96321751-96402018-08-0112557057710.1049/iet-cvi.2017.0394DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language datasetGarima Joshi0Renu Vig1Sukhwinder Singh2Electronics and Communication Engineering Department, UIET, Sector‐25Panjab UniversityChandigarhIndiaElectronics and Communication Engineering Department, UIET, Sector‐25Panjab UniversityChandigarhIndiaComputer Science and Engineering Department, UIET, Sector‐25Panjab UniversityChandigarhIndiaSign language recognition system classifies signs made by hand gestures. An adequate number of features are required to represent the shape variations of sign language. As compared to individual feature set, a combination of features can be effective due to the fact that a particular feature set represents different shape information. A simple concatenation results in large feature vector size and increases the classification computational complexity. Discriminant correlation analysis (DCA)‐based unimodal feature‐level fusion has been applied on uniform as well as complex background Indian sign language datasets. DCA is a feature‐level fusion technique that takes into account the class associations while combining the feature sets. It maximises the inter‐class separability of two feature sets and also minimises the intra‐class separability while performing the feature fusion. The objective of DCA‐based unimodal feature fusion technique is to combine different feature sets into a single feature vector with more discriminative power. The performance of proposed framework is compared with individual orthogonal moment‐based feature sets and canonical correlation analysis (CCA)‐based feature fusion technique. Results show that in comparison to individual features and CCA‐based fused features, DCA is an effective technique in terms of improved accuracy, reduced feature vector size and smaller classification time.https://doi.org/10.1049/iet-cvi.2017.0394DCA-based unimodal feature-level fusionorthogonal momentsIndian sign language datasetsign language recognition systemhand gesturesshape variations |
| spellingShingle | Garima Joshi Renu Vig Sukhwinder Singh DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset IET Computer Vision DCA-based unimodal feature-level fusion orthogonal moments Indian sign language dataset sign language recognition system hand gestures shape variations |
| title | DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset |
| title_full | DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset |
| title_fullStr | DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset |
| title_full_unstemmed | DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset |
| title_short | DCA‐based unimodal feature‐level fusion of orthogonal moments for Indian sign language dataset |
| title_sort | dca based unimodal feature level fusion of orthogonal moments for indian sign language dataset |
| topic | DCA-based unimodal feature-level fusion orthogonal moments Indian sign language dataset sign language recognition system hand gestures shape variations |
| url | https://doi.org/10.1049/iet-cvi.2017.0394 |
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