अमूर्त

Neuro Fuzzy Classifier for Image Retrieval

S.Asha, S.Ramya, M.Sarulatha, M.Prakasham, P.Priyanka

Content-based image retrieval system has been an active research topic in areas such as, entertainment, multimedia, education, image classification and searching. One of the key issues with the Content-based image retrieval system is to extract essential information from the raw data which reflect the image content. Even though large numbers of feature extraction and retrieval techniques have been developed, there are still no globally accepted techniques available for region/object representation and retrieval. In this paper, we propose an Adaptive neuro fuzzy inference system (ANFIS), it has a potential to capture both benefits of neutral network and fuzzy logic. The Color extraction is done based on RGB (red, green, blue), HSV (hue, saturation value) and Y,Cb,Cr (luminance and chrominance).Texture of an image is extracted by Gray Level Co-Occurrence Matrix (GLCM) which is a popular statistical method. Shape extraction of an image can be determined by Canny Edge Detection. Thus the experimental results may show that our retrieval framework is very effective and requires less computation time with an unique systemic processes and outperforms the conventional image retrieval systems. The experiment results are analyzed based on the Corel Datasets.