Fourler Dönüşüm Tabanlı Betimleyici Kullanarak Görsel Nesne Sınıflandırma
Özet
Most of the state-ofarts visual object classification
methods use bag of words model for image representation. In
this method, patches extracted from images are described by
different shape and texture descriptors such as SIFT, LBP,
SURF, etc. In this paper we introduce a new descriptor based
on weighted histograms of phase angles of local Fourier
transform (FT). We compare the classification accuracies
obtained by using the proposed descriptor to the ones
obtained by other well-known descriptors on Caltech-4 and
Coil-IOO data sets. Experimental results show that our
proposed descriptor provides good accuracies indicating that
FT based local descriptor captures important characteristics
of images that are useful for classification. When we
combined image representations obtained by FT descriptor
with the representations obtained by other descriptors, results
even get beUer suggesting that tested descriptors encode
differential complementary information.
