Abstract
Image recognition on streaming data is one of the most challenging topics in Image and Video Technology and incremental dimensionality reduction algorithms play a key role in online image recognition. In this paper, we present a novel supervised dimensionality reduction algorithm—Incremental Weighted Karhunen-Loève expansion based on the Between-class scatter matrix (IWKLB) for image recognition on streaming data. In comparison with Incremental PCA, IWKLB is more effective in terms of recognition rate. In comparison with Incremental LDA, it is free of small sample size problems and can directly be applied to high-dimensional image spaces with high efficiency. Experimental results conducted on AR, one benchmark face image database, demonstrate that IWKLB is more effective than IPCA and ILDA.
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Song, F., Zhang, D., Chen, Q., Yang, J. (2006). A Novel Supervised Dimensionality Reduction Algorithm for Online Image Recognition. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_20
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DOI: https://doi.org/10.1007/11949534_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
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