Abstract
We propose a novel texture feature extraction technique based on coefficients' co-occurrence histogram of discrete wavelet frame transformed image, which capture the information about relationship between each high frequency subband and the low frequency subband of the decomposed image at the corresponding level. It is not independently utilizing the information of each subband coefficient. The classification performance is analyzed using the k-NN classifier. And the experimental results demonstrate the effectiveness of our proposed texture feature in achieving the improved classification performance. Comparisons with the Gabor filter and a recently proposed approach are also provided.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
