Author(s)

Ammar Huneiti, Maisa Daoud

Department of Computer Information Systems, the University of Jordan, Amman, Jordan.

Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via SOM. The proposed method demonstrated promising retrieval results on the Wang Database compared to the existing methods in literature.

Image Retrieval, SOM, DWT, Feature Vector, Texture Vector

Huneiti, A. and Daoud, M. (2015) Content-Based Image Retrieval Using SOM and DWT. Journal of Software Engineering and Applications, 8, 51-61. doi: 10.4236/jsea.2015.82007.