A PSO based adaboost approach to object detection
| aut.researcher | Mohemmed, Ammar | |
| dc.contributor.author | Mohemmed, AW | |
| dc.contributor.author | Zhang, M | |
| dc.contributor.author | Johnston, M | |
| dc.date.accessioned | 2012-04-02T03:41:28Z | |
| dc.date.available | 2012-04-02T03:41:28Z | |
| dc.date.copyright | 2008 | |
| dc.date.issued | 2008 | |
| dc.description.abstract | This paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two PSO based methods in this paper. The first uses PSO to evolve and select the good features only and the weak classifiers use a kind of decision stump. The second uses PSO for both selecting the good features and evolving weak classifiers in parallel. These two methods are examined and compared on a pasta detection data set. The experiment results show that both approaches perform quite well for the pasta detection problem, and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only for this problem. | |
| dc.identifier.citation | Lecture Notes in Computer Science, Vol. 5361, 81-90 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | https://hdl.handle.net/10292/3566 | |
| dc.publisher | Springer Verlag | |
| dc.relation.uri | http://www.springerlink.com/content/k065r75773425654/about/ | |
| dc.rights | An author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation) | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Particle swarm optimisation | |
| dc.subject | AdaBoost | |
| dc.subject | Object classification | |
| dc.subject | Object recognition | |
| dc.title | A PSO based adaboost approach to object detection | |
| dc.type | Conference Contribution | |
| pubs.organisational-data | /AUT | |
| pubs.organisational-data | /AUT/Design & Creative Technologies | |
| pubs.organisational-data | /AUT/PBRF Researchers | |
| pubs.organisational-data | /AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers | |
| pubs.organisational-data | /AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT Kedri |