Multi-dimensional keyword-based image annotation and search

Abstract

Effective keyword search on image databases is a major open problem, due to the inherent imprecision of keywords (tags) used to describe images' content. In this paper we present a novel approach to deal with this problem, as implemented in the Scenique image retrieval and browsing system. Scenique is based on a multi-dimensional model, where each dimension is a tree-structured taxonomy of concepts, also called semantic tags, that are used to describe the content of images. We first describe an original algorithm, called Ostia (Optimal Semantic Tags for Image Annotation), that, by exploiting low-level visual features, tags, and metadata associated to an image, is able to predict a high-quality set of semantic tags for that image. Then, we describe how semantic tags can be effectively used for the purpose of improving the precision of keyword search. © 2010 ACM.