Image Retrieval using Upper Mean Color and Lower Mean Color of Image
N.S.T. Sai, R. C. Patil, Mukesh T. Patel · 2012
This present the new idea of image retrieval using upper mean color and lower mean color of image as feature vector. Feature vector calculated by using bit planes of every image. This paper compares performance of 4 bit planes and 8 bit planes for gray scale and color image. So feature vector of proposed method is varies in accordance with the number of bit planes used. Proposed method tested on the database which includes 930 images having 10 different classes. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. The average precision and average recall of each image category and over all precision and recall is considered for the performance measure. II. Bit Plane Slicing Bit-Plane Slicing Image enhancement is the method to enhance the image which is of low contrast. But the drawback in this method is that all the pixels in the image are brightened totally and this may not be suitable for some applications [7]. So to overcome this, Bit-Plane Slicing method is used. Bit-Plane Slicing is a technique in which the image is sliced at different planes. It ranges from Bit level 0 which is the Least Significant Bit (LSB) to Bit level 7 which is the Most Significant Bit (MSB) as shown in fig.1.