Abstract
In the paper an approach to shape classification and shape retrieval is described. Although, most of available shape descriptors give a very good recognition accuracy or retrieval rate, they suffer from one serious limitation, namely, they do not take into account the dimensionality of feature space, hence the computational costs of similarity evaluation is rather high. The problem occurs often in the hardware implementations, where the complexity of processed data should be minimized. Hence we propose a method of joining low-dimensional feature vectors derived from shapes to increase the retrieval rate and classification accuracy.
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Forczmański, P. (2015). Improving Shape Retrieval and Classification Rates through Low-Dimensional Features Fusion. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_5
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DOI: https://doi.org/10.1007/978-3-319-10662-5_5
Publisher Name: Springer, Cham
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