Step1: Extract the Red, Green, and Blue Components from an image.
Step2: Decompose each Red, Green, Blue Component using Haar Wavelet transformation at 1st
level to get approximate
coefficient and vertical, horizontal detail coefficients.
Step3: Combine approximate coefficient of Red, Green, and Blue Component.
Step4: Similarly combine the horizontal and vertical coefficients of Red, Green, and Blue Component.
Step5: Assign the weights 0.003 to approximate coefficients, 0.001 to horizontal and 0.001 to vertical coefficients
(experimentally observed values).
Step6: Convert the approximate, horizontal and vertical coefficients into HSV plane.
Step7: Color quantization is carried out using color histogram by assigning 8 level each to hue, saturation and value to give a
quantized HSV space with 8x8x8=512 histogram bins.
Step8: The normalized histogram is obtained by dividing with the total number of pixels.
Step9: Repeat step1 to step8 on an image in the database.
Step 10: Apply K-means algorithm to obtain group of cluster of feature vectors.
Step 11: Then hierarchical index will be firstly established by Hierarchical clustering algorithm on the results of previous
step, and then retrieval will be done based on the indexing. We use the Euclidean distance formula as similarity