EFFICIENT RETRIEVAL TECHNIQUES FOR IMAGES USING ENHANCED UNIVARIATE TRANSFORMATION APPROACH
S. P. Victor, V. Narayani, S. Rajkumar · 2010
Abstract Image mining is a process to find valid, useful, and understandable knowledge from large image sets or image databases. Image mining combines the areas of content-based image retrieval, image understanding, data mining and databases. Image mining deals with the extraction of knowledge, image data relationship, or other patterns not explicitly stored in the images. It uses methods from computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Rule mining has been applied to large image databases. Image mining is more than just an extension of data mining to image domain. Keywords Image mining, preprocessing, segmentation, objects, pixels, clusters I Introduction Feature selection and extraction is the pre-processing step of Image Mining. This is a critical step in Image Mining. The approach here is to mine from Images – to extract patterns and derive knowledge from large collections of images, deals mainly with identification and extraction of unique features for a particular domain. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images. A. Component Recognition Component recognition requires making the step from raster to objects. It is done by applying a segmentation in which objects are modeled. Image segmentation can be performed by various procedures based on mathematical morphology, edge detection. Interesting results can be obtained by applying texture based segmentation. The result of this operation for an image at moment t thus is a series of n