A New Method for Data Mining in Multimedia Environment

Most of the existing frequent item sets mining techniques are based up on Multimedia data mining. In this paper we propose a novel approach for frequent item sets mining using color, texture and shape. Frequent item set is an item set that satisfies minimum support. The data bases tested in the Multimedia Miner System is constructed. Each Image contains two descriptors: a feature descriptor and a Layout descriptor. The original Image is not stored directly in the database only its descriptors are stored. The description information encompasses fields like Image file name, Image URL, Image type like gif, tiff, jpeg, mpeg, bmp, avi. A list of all known web pages referring to the image i.e parent URLs. A list of keywords and a thumbnail used by the user interface for image and video Browsing. The feature descriptor is set of vectors for each visual characteristic. The main vectors are a color vector containing the color histogram quantized to 512 colors (8*8 for R*G*B, an Most Frequent Color vector and Most Frequent Orientation Vector. The image Excavator component of Multimedia Minor uses image contextual information.