Color and Edge Directive Descriptor Feature Extraction Technique for Content Based Image Retrieval System

The development of multimedia technology in Content Based Image Retrieval (CBIR) System is one of the prominent area to retrieve the images from a large collection of database. It is practically observed that any one algorithm is not efficient in extracting all different types of natural images. Hence a thorough analysis of certain color, texture and edge extraction techniques are carried out to identify an efficient CBIR technique which suits for a particular type of images. The Extraction of an image includes feature description, index generation and feature detection. The low-level feature extraction technique is proposed in this paper are tested on Corel database, which contains 10 categories of natural image dataset, each category has 100 images, totally the database has 1000 images. The feature vectors of the query image are compared with feature vectors of the database images to obtain matching images. This paper proposes Color and Edge Directivity Descriptor (CEDD) feature extraction technique which extract the matching image based on the similarity of color and edge of an image in the database. The Image Retrieval Precision (IRP) and Recall value of the proposed technique is calculated and compared with that of the existing techniques. The algorithms used in this paper are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Fuzzy Linking algorithm. The proposed technique results in the improvement of the average Precision and Recall value. Also CEDD is effective and efficient for image indexing and image retrieval.