Texture classification using wavelet extraction: An approach to haptic texture searching

S., Sulaiman and W., Adi (2009) Texture classification using wavelet extraction: An approach to haptic texture searching. In: 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, 25 July 2009 through 26 July 2009, Kuala Lumpur.

[img] PDF
Restricted to Registered users only


Official URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...


while visual texture classification is a widely-research 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. © 2009 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Accuracy rate; Energy signatures; Extracting information; Feature vectors; Haptic texture search engine; Haptic textures; Machine learning; Research topics; Sub-bands; Testing data; Texture classification; Texture recognition; Transformation coefficients; Visual texture; Education; Feature extraction; Image analysis; Industrial applications; Information retrieval; Intelligent systems; Robot learning; Search engines; Supervised learning; Textures; World Wide Web; Wavelet decomposition
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:92
Deposited By: Dr Suziah Sulaiman
Deposited On:23 Feb 2010 04:04
Last Modified:19 Jan 2017 08:25

Repository Staff Only: item control page

Document Downloads

More statistics for this item...