OPTIMASI QUERY CBIR MENGGUNAKAN INDEKS KLASTER PADA DATABASE CITRA - , Juli Rejito, Drs.,M.Kom. ([Yogyakarta] : Universitas Gadjah Mada, 2013)
The increasing number of digital images stored in the computer storage media and database capabilities to provide users information needs quickly and accurately has become a necessity. Thus the efficiency in searching large image databases on the system CBIR (content-based image retrieval) becomes indispensable. This research is aimed to improve the accuracy of image retrieval and content access time optimization applied to WANG database as much as 1,000 image records and image database as much as 48,000 image records in resolution ranges between 256 x 384 to 3,727 x 7,787 pixels. The solution developed form of system architecture is an integration of query optimization and clustering partitions. The cluster formation basis in this study utilized the minimum and maximum PSNR (peak signal to noise ratio) values in form of image similarity comparison result between the color feature extraction image database record and basic image, the result is stored as a table in the database image at once functioned as a cluster index for filtering records and positioning cluster. Clustering results were then tested for validity using the Davies Bouldin index and Dunn's index to determine the optimum cluster. Testing image content retrieval to measure the level of accuracy in image databases using the 4 color feature extraction namely PSNR color, color intensity, color histogram and color moment. The level of accuracy of the content based image retrieval using the WANG database stated in F-Score value for query using 5 clusters in K-Means clustering showed improved performance 0.2278 compared to query without cluster only 0,1428. Similarly, a query using 4 clusters on the clustering CLARA demonstrate improved performance becomes 0.2272. The result speed in access duration significantly went down in formation of 64 clusters as 99,74% in comparison with noncluster query in image database.