There is a problem that the image features extracted by trace transform lack description of texture edge information

and the computation cost is high

too.Based on the advantages of wavelet transform in image contour representation

a new fusion feature extraction algorithm

multi-resolution trace transform

is proposed and applied to texture image classification.Firstly

the wavelet transform was introduced in trace transform

low frequency feature sub images and high frequency edge sub images of texture images at different frequencies are obtained by using nonsubsampled wavelet transform.Then

we carried out a set of functional trace transform on each level sub images to obtain the fusion features of texture image

which not only obtains the edge information of the image

but also avoids the problem of high cost.Finally

the fusion features were fed into support vector machines to classify the images.The experiment results show that the fusion features of multi-resolution trace transformation have better texture description ability and achieves higher recognition rate than the original trace transform and MCM contrast method

the time efficiency is greatly improved compared to the traditional trace transform.