Efficient Color and Texture Feature Extraction Technique for Content Based Image Retrieval System

The future user needs in the field of multimedia retrieval is the focus of many research and development activists. It is empirically observed that no single algorithm is efficient in extracting all different types of images like building images, flower images, car images and so on. Hence, a thorough analysis of certain color, texture and shape extraction techniques are carried out to identify an efficient Content Based Image Retrieval (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 techniques are proposed in this paper are tested on Corel database, which contains 1000 images. The feature vectors of the Query Image (QI) are compared with feature vectors of the database images to obtain Matching Images (MI). This paper proposes Fuzzy Color and Texture Histogram (FCTH), and Color and Edge Directivity Descriptor (CEDD) techniques which extract the matching image based on the similarity of color and edge of an image in the database. The Image Retrieval Precision value (IRP) of the proposed techniques are 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 value. Also FCTH and CEDD are effective and efficient for image indexing and image retrieval.