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dc.contributor.authorLiang, Antoni
dc.contributor.authorAn, Senjian
dc.contributor.authorLiu, Wan-Quan
dc.identifier.citationLiang, Antoni and An, Senjian and Liu, Wanquan. 2013. Efficient sub-window search with fixed shape sub-windows. International Journal of Machine Learning and Cybernetics. 4 (1): pp. 41-49.

This paper addresses the performance improvement of efficient sub-window search algorithms for object detection. The current algorithms are for flexible rectangle-shaped sub-window with high computation costs. In this paper, a restriction is applied on the sub-window shape from rectangle into square in order to reduce the number of possible sub-windows with an expectation to improve the computation speed. However, this may come with a consequence of accuracy loss for some objects. In addition, another variance of sub-window shape is also tested which based on the ratio between the height and width of an image. The experiment results on the proposed algorithms were analysed and compared with the performance of the original algorithms to determine whether the speed improvement is significantly large while making the accuracy loss acceptable. It was found that some new algorithms show a good speed improvement while maintaining small accuracy loss. Furthermore, there is an algorithm designed from a combination of a new algorithm and an original algorithm which gains the benefit from both algorithms and produces the best performance among all new algorithms.

dc.titleEfficient sub-window search with fixed shape sub-windows
dc.typeJournal Article
dcterms.source.titleInternational Journal of Machine Learning and Cybernetics
curtin.accessStatusFulltext not available

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