Extraction of Texture features Using Euclidean, Canberra and Both Distance
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Abstract
In this paper Cosine-modulated class of multiplicity M wavelet tight frames (WTF_s). In these WTF_s, the scaling function uniquely
determines the wavelets. This is in contrast to general multiplicity M case, where one has to, for any given application, design the scaling
function and the wavelets. Hsin used a modulated wavelet transform approach for texture segmentation and reported that texture segmentation
performance can be improved with this approach. Guillemot and Onno had used Cosine-modulated wavelet for image compression. They have
presented procedure for designing Cosine-modulated wavelets for arbitrary length filters. This procedure allows obtaining filters with high
stopband attenuation even in the presence of additional regularity constraints. Their results show that these filter solution provide good
performance in image compression. The advantages of the Cosine-modulated wavelet are their low design and implementation complexities,
good filter quality, and ease in imposing the regularity conditions, which yields improved retrieval performance both in terms of accuracy and
retrieval time. Feature extraction is one of the most important tasks for efficient and accurate image retrieval purpose. In this paper we are going
to use Cosine-modulated wavelet transform based technique for extraction of texture features. The major advantages of Cosine-modulated
wavelet transform are less implementation complexity, good filter quality, and ease in imposing the regularity conditions. Texture features are
obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval
performance, texture database of 1856 textures is created from Brodatz album. Retrieval efficiency and accuracy using Cosine-modulated
wavelet based features will be found to be superior to other existing methods.
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Keywords: Cosine-modulated wavelet; Content-based image retrieval; Image database; Query image; Texture analysis
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