Abstract
Research in this paper is focused to make a change on variety of Efficient Sub-window Search algorithms. 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. 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 to make 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.
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References
An, S., Peursum, P., Liu, W., Venkatesh, S.: Efficient Algorithms for Subwindow Search in Object Detection and Localization. In: IEEE Conferences on Computer Vision and Pattern Recognition, Miami, pp. 264–271 (2009)
An, S., Peursum, P., Liu, W., Venkatesh, S., Chen, X.: Exploiting Monge Structures in Optimum Subwindow Search. In: IEEE Conferences on Computer Vision and Pattern Recognition, San Fransisco, pp. 926–933 (2010)
Bentley, J.: Programming Pearls: Perspective in Performance. Commun. ACM 27, 1087–1092 (1984)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In: IEEE Conferences on Computer Vision and Pattern Recognition, Anchorage, pp. 1–8 (2008)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2129–2142 (2009)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
The PASCAL Visual Object Classes Challenge 2006 (VOC 2006) Results, http://www.pascal-network.org
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Liang, A., An, S., Liu, W. (2011). Fast Sub-window Search with Square Shape. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_55
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DOI: https://doi.org/10.1007/978-3-642-25832-9_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
Online ISBN: 978-3-642-25832-9
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