Computer - Sciene

Content-based image retrieval has been an active area of research over last decade. The goal is to create systems capable of interactively retrieving images that are semantically related to the user's query from a database. In this research, a content-based image retrieval system is presented, it supports querying by example to retrieve images from the images database according to their color and textural low level features.The underlying techniques are based on the adoption of histograms of YUV, YIQ, YCbCr, HSV and HSI color models as color features.

The classification process is an important task in many application of computer image analysis for classifying images based on color or texture low-level features.In this work, a texture classification system is presented which supports querying with respect to texture low-level feature. The fundamental idea is to generate automatically image description by analyzing the image content. The underlying techniques are based on the Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) as statistical approaches to texture analysis.

 Text mining has become an important research issue since the explosion of digital and online text information, where data might be stored as electronic documents or as text fields in databases, text mining has increased in importance and economic value. One of the important goals in text mining is automatic classification of electronic documents.