A relational vector space model using an advanced weighting scheme for image retrieval

In this paper, we lay out a relational approach for indexing and retrieving photographs from a collection. The increase of digital image acquisition devices, combined with the growth of the World Wide Web, requires the development of information retrieval (IR) models and systems that provide fast access to images searched by users in databases. The aim of our work is to develop an IR model suited to images, integrating rich semantics for representing this visual data and user queries, which can also be applied to large corpora.Our proposal merges the vector space model of IR – widely tested in textual IR – with the conceptual graph (CG) formalism, based on the use of star graphs (i.e. elementary CGs made up of a single relation connected to some concepts representing image objects). A novel weighting scheme for star graphs, based on image objects size, position, and image heterogeneity is outlined. We show that integrating relations into the vector space model through star graphs increases the system’s precision, and that the results are comparable to those from graph projection systems, and also that they shorten processing time for user queries.

Research Highlights
► Relational approach for indexing and retrieving photographs from a collection.
► Vector space model extension using conceptual graphs to include relations.
► Weighting scheme dedicated to image objects.