Image Retrieval using DWT on Row and Column Pixel Distribution of BMP Image and Row Mean and Column Mean

摘要

With the rapid development of technology of multimedia, the traditional information retrieval techniques based on keywords are not sufficient, content - based image retrieval (CBIR) has been an active research topic. The large amount of image collections available from a variety of sources has posed increasing technical challenges to computer systems to store/transmit and index/manage the image data to make such collections easily accessible. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval system. CBIR extracts the features of query image and try to match them with the extracted features of images in the database. Then based on the similarity measures and threshold the best possible candidate matches are given as result. Wavelet transform is used to generate feature vector based on the row and column wise pixel distribution of binary bit mapped image (BMP)and row and column mean value of R,G,B color plane of color image. Wavelet coefficients and moments of wavelet coefficients make a variable feature vector size. In this paper 4 novel techniques are discussed with their precision and recall performance. Simple Euclidean Distance used to compute the similarity measures of images for Content Based Image Retrieval application. The proposed image retrieval techniques are applied on a image database of 500 images include 5 classes. All this approaches gives acceptable results.