Saliency-Based Optimization for the Histogram of Oriented Gradients-Based Detection Methods

Conference paper

DOI: 10.1007/978-3-319-59162-9_16

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)
Cite this paper as:
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

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

Saliency HoG SIFT Feature points Object detection k-NN 

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Control and Industrial ElectronicsWarsaw University of TechnologyWarsawPoland

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