Using Wavelet Extraction for Haptic Texture Classification

While visual texture classification is a widely-researched topic in
image analysis, little is known on its counterpart i.e. the haptic (touch) texture.
This paper examines the visual texture classification in order to investigate how
well it could be used for haptic texture search engine. In classifying the visual
textures, feature extraction for a given image involving wavelet decomposition
is used to obtain the transformation coefficients. Feature vectors are formed
using energy signature from each wavelet sub-band coefficient. We conducted
an experiment to investigate the extent in which wavelet decomposition could
be used in haptic texture search engine. The experimental result, based on
different testing data, shows that feature extraction using wavelet
decomposition achieve accuracy rate more than 96%. This demonstrates that
wavelet decomposition and energy signature is effective in extracting
information from a visual texture. Based on this finding, we discuss on the
suitability of wavelet decomposition for haptic texture searching, in terms of
extracting information from image and haptic information.