A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching
Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similar...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/906861 |
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| author | Bai Li Li-Gang Gong Ya Li |
| author_facet | Bai Li Li-Gang Gong Ya Li |
| author_sort | Bai Li |
| collection | DOAJ |
| description | Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do. |
| format | Article |
| id | doaj-art-557b5e91ac6e4594a0acc026e6728e8a |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-557b5e91ac6e4594a0acc026e6728e8a2025-02-03T01:32:50ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/906861906861A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template MatchingBai Li0Li-Gang Gong1Ya Li2School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaSchool of Mathematics and Systems Science & LMIB, Beihang University, Beijing 100191, ChinaImage template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do.http://dx.doi.org/10.1155/2014/906861 |
| spellingShingle | Bai Li Li-Gang Gong Ya Li A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching The Scientific World Journal |
| title | A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching |
| title_full | A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching |
| title_fullStr | A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching |
| title_full_unstemmed | A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching |
| title_short | A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching |
| title_sort | novel artificial bee colony algorithm based on internal feedback strategy for image template matching |
| url | http://dx.doi.org/10.1155/2014/906861 |
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