Recursive algorithms content search in multimedia databases with endoscopic images

This is a comparative study for recursive algorithms in two content search steps in multimedia databases with endoscopic images. The performance of the search has been measured according to four parameters: reapel, precision, the quality of retrieval and the cost of retrieval. The algorithms are based two methods of deciding the similarities between the models of images: Minkowsky distance and correlated measure Pearson. The models of representing the images in multimedia data base used here are: the normalized color histogram and texture or granulation. The data base which contained the models is indexed according to a linear cluster type. The color space of the images are RGB reduced to 125 and 256 colors and HSV reduced to 162 and 256 colors. The study was realized in a data base containing 310 images grouped in 30 categories. The results are presented both in tables and graphs.