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2 Journal of Applied Mathematics and Computational Mechanics

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Novel visual object descriptor using surf and clustering algorithms

Grycuk R.

Journal of Applied Mathematics and Computational Mechanics

2016

Vol. 15, nr 3

37--46

In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage.

New image descriptor from edge detector and blob extractor

Grycuk R., Scherer R., Gabryel M.

Journal of Applied Mathematics and Computational Mechanics

2015

Vol. 14, nr 4

31--39

In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.