Hi,
I am using wavelet as feature descriptor. When I use small size subband like 16x16 then getting better result than using 32x32 sizes subband. When I take large size then I am losing edge information. Why?
Because it depends on the interval quasi-stationary of your signal. It Is possible for others type of data size 32 to 32 will be better.
While I am getting 16 by 16 better then how does it better for 32 by 32?
This can depend on various reasons such as spatial resolution of data.
as you are using small size wavelet i.e. small scale, it is use to detect high frequency in your signal. if you use wider wavelet, it will not detect the high frequencies in 1D or 2D signal.
Your question is a little bit fuzzy. The reason is that you are using maybe the wrong type of wavelets for your objects. It has nothing to do with your window size itself. Please make sure that you use the coorect wavelets for your edge detection. If your wavelet length is too long you will definitely not detect an edge in either direction. Your then have to take a look in other scales. Here is a paper which describe the effect of different wavelets on scales. In the references you will find more papers.
https://www.researchgate.net/publication/262698936_Banknote_authentication_with_mobile_devices
Conference Paper Banknote Authentication with Mobile Devices
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