TITLE:
Content-Based Image Retrieval Using SOM and DWT
AUTHORS:
Ammar Huneiti, Maisa Daoud
KEYWORDS:
Image Retrieval, SOM, DWT, Feature Vector, Texture Vector
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.2,
February
13,
2015
ABSTRACT: 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.