Colourhistogram II. TEXTURE FEATURE EXTRACTION IN CBIR An overview of the proposed CBIR system is illustrated in Fig. 1. The proposed algorithm, Label Wavelet Transform (LWT), is based on color image segmentation [1], and it is an extension of DWT-based texture feature extraction method. The 2-D DWT is computed by applying separable filter banks to the gray level images. The detail images Dn,1, Dn,2, and Dn,3 are obtained by band-pass filtering in a specific direction, and they can be categorized into three frequency bands: HL, LH, HH band, respectively. Each band contains different directional information at scale n. The texture feature is extracted from the variance (ó2 n,i) of the coefficients cn,i of the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To represent the texture feature of an image q, the texture feature vector of DWT is defined as [2]: TDWT (q) = [ó2 1,1, ó2 1,2, ó2 1,3, ..., ó2N max,3], (1) where Nmax denotes the largest scale. In this work, Nmax

colourhistogram.txt

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．