Feature Extraction Techniques for Image Retrieval Using Data Mining and Image Processing Techniques

Abstract: Image mining is advancement in the field of data mining, in the domain of Image processing. Image mining is the additional pattern which is quite not clearly visible in the image, association of image data and extraction of hidden data. This field is interrelated and involves Database, Artificial Intelligence, Data Mining, Machine Learning, and Image processing. Image Mining has a lucrative point that without any information of the patterns it can generate all the significant patterns. This writing is done for a research on the data mining techniques and assorted image mining. The term data mining is the extraction of information/ knowledge from a wide database which is further stored in multiple heterogeneous databases. Information/ Knowledge are conveying of message through direct or indirect methods. These methods include clustering, correlation, association and neural network. This thesis provides with a basic informatory review on the applied fields of data mining which is varied into manufacturing, telecommunication, education, fraud detecting and marketing sector. In this method we use texture, dominant colour factors and size of an image. The feature which is used to determine the image texture is called as Gray Level Co-occurrence Matrix (GLCM). The texture, color and such relative features are normalized. Due to the use of the texture and color feature of the attached image due to the shape feature the image retrieval feature will be very sharp. Weighted Euclidean distance of color feature is utilized for the retrieving of features, of similar types of image shape and texture feature.

Keywords: Data Mining, Image Mining, Feature Extraction, Image Retrieval, Association, Clustering, knowledge discovery database, Gray Level Co-occurrence Matrix, centroid, Weighted Euclidean Distance.