Scholars' Mine
Missouri S&T
Research Repository
Curtis Laws Wilson Library
400 W. 14th Street
Rolla, MO 65409-0060
scholarsmine@mst.edu
| Title: | Permutation coding technique for image recognition systems | |
| Author (s): | Kussul, E. M. Baidyk, T. N. Wunsch, Donald C. Makeyev, O. Martn, A. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering | |
| Keywords: | MNIST database Olivetti Research Laboratory (ORL) database face recognition feature extraction handwritten digit recognition image coding image recognition image recognition systems neural classifier neural nets permutation coding neural classifier permutation coding technique random local descriptors | |
| Issue Date: | 2006 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Kussul, E. M.; Baidyk, T. N.; Wunsch II, D. C.; Makeyev, O.; Martn, A. "Permutation Coding Technique for Image Recognition Systems" IEEE Transactions on Neural Networks, Vol.17, Iss.6, Nov. 2006 Pages:1566-1579 | |
| Abstract: | A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1% | |
| Type: | Article - Journal text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
| Publisher URL: | ||
| Link to this page: | ||
| Full Text: |
|
| title | Permutation coding technique for image recognition systems | |
| contributor.author | Kussul, E. M. | |
| contributor.author | Baidyk, T. N. | |
| contributor.author | Wunsch, Donald C. | |
| contributor.author | Makeyev, O. | |
| contributor.author | Martn, A. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | MNIST database | |
| subject | Olivetti Research Laboratory (ORL) database | |
| subject | face recognition | |
| subject | feature extraction | |
| subject | handwritten digit recognition | |
| subject | image coding | |
| subject | image recognition | |
| subject | image recognition systems | |
| subject | neural classifier | |
| subject | neural nets | |
| subject | permutation coding neural classifier | |
| subject | permutation coding technique | |
| subject | random local descriptors | |
| date.issued | 2006 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Kussul, E. M.; Baidyk, T. N.; Wunsch II, D. C.; Makeyev, O.; Martn, A. "Permutation Coding Technique for Image Recognition Systems" IEEE Transactions on Neural Networks, Vol.17, Iss.6, Nov. 2006 Pages:1566-1579 | |
| identifier.issn | 1045-9227 | |
| identifier.pub.URI | ||
| description.abstract | A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1% | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:28:28Z | |
| date.available | 2007-04-05T14:28:28Z | |
| identifier.persist.URI | ||
| Full Text |
|