Abstract
The paper presents research on using graphical saliency cue for optimizing computation of the distance metrics for HoG features. Saliency values have been computed in the area of a HoG descriptor for macro and micro scale. Macro scale uses HoG features as a global descriptor for an image presenting a particular object, whereas micro scale consists of feature points and k-nearest neighbours approach to create similarity measure. Mechanism has been tested on a chosen database consisting of 20 000 images. Promising results have been achieved for macro scale approach.
Keywords
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Kurzejamski, G., Iwanowski, M. (2018). Saliency-Based Optimization for the Histogram of Oriented Gradients-Based Detection Methods. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_16
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