A database centric view of semantic image annotation and retrieval

We introduce a new model for semantic annotation and retrieval from image databases. The new model is based on a probabilistic formulation that poses annotation and retrieval as classification problems, and produces solutions that are optimal in the minimum probability of error sense. It is also database centric, by establishing a one-to-one mapping between semantic classes and the groups of database images that share the associated semantic labels. In this work we show that, under the database centric probabilistic model, optimal annotation and retrieval can be implemented with algorithms that are conceptually simple, computationally efficient, and do not require prior semantic segmentation of training images. Due to its simplicity, the annotation and retrieval architecture is also amenable to sophisticated parameter tuning, a property that is exploited to investigate the role of feature selection in the design of optimal annotation and retrieval systems. Finally, we demonstrate th...

• Type: Conference
• Year: 2005
• Where: SIGIR
• Authors: Gustavo Carneiro, Nuno Vasconcelos
• Keywords: Annotation, Optimal Annotation, Semantic, SIGIR 2005