Description: 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
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