Article,
Image Retrieval using Equalized Histogram Image Bins Moments
International Journal on Signal & Image Processing, 2 (1): 4 (January 2011)
Abstract
CBIR operates on a totally different principle
from keyword indexing. Primitive features characterizing
image content, such as color, texture, and shape are computed
for both stored and query images, and used to identify the
images most closely matching the query. There have been
many approaches to decide and extract the features of images
in the database. Towards this goal we propose a technique by
which the color content of images is automatically extracted to
form a class of meta-data that is easily indexed. The color
indexing algorithm uses the back-projection of binary color
sets to extract color regions from images. This technique use
without histogram of image histogram bins of red, green and
blue color. The feature vector is composed of mean, standard
deviation and variance of 16 histogram bins of each color
space. The new proposed methods are tested on the database
of 600 images and the results are in the form of precision and
recall.
