Abstract
We investigate serial multiple classifier system architectures which exploit a hierarchical output coding. Such architectures are known to deliver performance benefits and are widely used in applications involving a large number of classes such as character and handwriting recognition. We develop a theoretical model which underpins this approach to multiple classifier system design and show how it relates to various heuristic design strategies advocated in the literature. The approach is applied to the problem of 3D object recognition in computer vision.
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References
A. Ahmadyfard and J. Kittler. Enhancement of ARG object recognition method. In Proceeding of 11 the European Signal Processing Conference, volume 3, pages 551–554, September 2002.
A. R. Ahmadyfard and J. Kittler. Amultiple classifier system approach to affine invariant object recognition. In 3rd International Conference on Computer Vision Systems, 2003 (accepted).
M C Fairhurst and A F R Rahman. Generalised approach to the recognition of structurally similar handwritten characters using multiple expert classifiers. IEE Proc. on Vision, Image and Signal Processing, 144(1):15–22, 2 1997.
http://www.ee.surrey.ac.uk/Research/VSSP/demos/colour/soil47/.
Ianakiev K. and V. Govindaraju. Architecture for classifier combination using entropy measures. In IAPR International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, pages 340–350, June 2000.
G Kim and V Govindaraju. A lexicon driven approach to handwritten word recognition. IEEE Transactions on PAMI, 19(4):366–379, 1997.
I Krassimir and V Govindaraju. An architecture for classifier combination using entropy measure. In J Kittler and F Roli, editors, Proceedings of Multple Classifier Systems 2000, pages 340–350, 2000.
S Madhvanath, E Kleinberg, and V Govindaraju. Holistic verification for handwritten phrases. IEEE Transactions on PAMI, 21(12):1344–1356, 1999.
J. Matas, D. Koubaroulis, and J. Kittler. Colour image retrieval and object recognition using the multimodal neighbourhood signature. In D. Vernon, editor, Proceedings of ECCV, volume Springer, pages 48–64, 2000.
A F R Rahman and M C Fairhurst. Exploiting second order information to design a novel multiple expert decision combination platform for pattern classification. Electronic Letters, 33:476–477, 1997.
A F R Rahman and M C Fairhurst. A new hybrid approach in combining multiple experts to recognise handwritten numerals. Pattern Recognition Letters, 18:781–790, 1997.
A F R Rahman and M C Fairhurst. An evaluation of multi-expert configurations for for the recognition of handwritten numerals. Pattern Recognition, 31:1255–1273, 1998.
A F R Rahman and M C Fairhurst. Enhancing multiple expert decision combination strategies through exploitation of a priori information sources. In IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, volume 146, pages 40–49, 1999.
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Kittler, J., Ahmadyfard, A., Windridge, D. (2003). Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_11
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DOI: https://doi.org/10.1007/3-540-44938-8_11
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