AN EFFICIENT FRAMEWORK FOR IMAGE DATA RECOGNITION & RETRIEVAL

Valuable information can be hidden in images, however, few research discuss data mining on them. Image retrieval means searching, browsing and retrieving images from image databases. There are two different methodologies for image retrieval i.e. text based image retrieval and content based image retrieval. Former one is obsolete. In latter one many visual features like texture, size, intensities, and frequency of pixels and color of image are extracted. In queryby-example search extracted featured are compared with stored ones. In this work an efficient for extracting image features is considered using intensity histogram of gray color image. Here in this general framework based on the decision tree for mining and processing image data. Pixel wised image features were extracted and transformed into a database-like table which allows various data mining algorithms to make explorations on it. Finally results of average gradient vectors are to be compared with previously stored one dimensional array of intensities to find similarities in image data.