Images represent a key source of information in many domains and the ability to exploit them through their discovery, analysis and integration by services and agents on the Semantic Web is a challenging and significant problem. To date the semantic indexing of images has concentrated on applying machine-learning techniques to a set of manually-annotated images in order to automatically label images with keywords. In this paper we propose a new hybrid, user-assisted approach, Rules-By-Example (RBE), which is based on a combination of RuleML and Query-By-Example. Our RBE user interface enables domain-experts to graphically define domain-specific rules that can infer high-level semantic descriptions of images from combinations of lowlevel visual features (e.g., color, texture, shape, size of regions) which have been specified through examples. Using these rules, the system is able to analyze the visual features of any given image from this domain and generate semantically meaningful labels, using terms defined in the domain-specific ontology. We believe that this approach, in combination with traditional solutions, will enable faster, more flexible, cost-effective and accurate semantic indexing of images and hence maximize their potential for discovery, re-use, integration and processing by Semantic Web services, tools and agents.
In REF a system is presented that allows domain experts to define rules on primitive visual features, such as colour or simple shapes, in order to annotate images automatically.
Rules-By-Example - a Novel Approach to Semantic Indexing and Querying of Images
In International Semantic Web Conference
Computer Science