Novel visual object descriptor using surf and clustering algorithms

• Autorzy: Grycuk R.

Identyfikatory

DOI

10.17512/jamcm.2016.3.04

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

k-means; mean shift; clustering; image description; SURF; keypoints; content-based image retrieval (CBIR)

PL

opis obrazu; algorytmy grupowania; detekcja punktów kluczowych

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2016
• Tom: Vol. 15, nr 3
• Strony: 37--46
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Grycuk R.

• Institute of Computational Intelligence, Czestochowa University of Technology Częstochowa, Poland

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.