Automated image annotation system based on an open source object database

Abstract

Automated annotation of digital images is a challenging task being used for indexing, retrieving, and understanding of large collections of image data. Several machine-learning approached have been proposed to model the existing associations between words and images. Each approach is trying to assign to a test image some meaningful words taking into account a set of feature vectors extracted from that image. This paper presents an original image annotation system based on an open source object database called db4o. An object oriented model offers suport for storing complex objects as sets, lists, trees or other advanced data structures. The information needed for the annotation process is retrieved from the SAIAPR TC-12 Dataset - a set of annotated images having a vocabulary with a hierarchical structure. The annotation system is using an efficient annotation model called Cross Media Relevance Model. © 2011 Springer-Verlag.