The binary form of an image does not tell what the image is about. It is possible to retrieve images from a database using pattern matching techniques, but usually textual descriptions attached to the images are used. Semantic web ontology and metadata languages provide a new way to annotating and retrieving images. This paper considers the situation when a user is faced with an image repository whose content is complicated and semantically unknown to some extent. We show how ontologies can then be of help to the user in formulating the information need, the query, and the answers. As a proof of the concept, we have implemented a demonstrational photo exhibition using the promotion image database of the Helsinki University Museum based on semantic web technologies. In this system, images are annotated according to ontologies and the same conceptualization is offered to the user to facilitate focused image retrieval using the right terminology. When generating answers to the queries, the ontology combined with the image data also

This paper describes how low-level statistical visual features can be analyzed in our content-based image retrieval system named PicSOM. The lowlevel visual features used in the system are all statistical by nature. They include average color, color moments, contrast-type textural feature, and edge histogram and Fourier transform based shape features. Other features can be added easily. A genuine characteristic of the PicSOM system is to use relevance feedback from the human user's actions to direct the system in scoring the relevance of particular features in the present query. While the link from features to semantic concepts remains an open problem, it is possible to relate low-level features to subjective image similarity, as perceived instantaneously by human users. The e#cient implementation of PicSOM allows tests using statistically su#- ciently large and representative databases of natural images. Acknowledgement This work was supported by the Finnish Centre of Excellence Pro...

Abstract. In this paper an application of the TS-SOM variant of the self-organising map algorithm on the problem of copyright theft detection for bitmap images is shown. The algorithm facilitates the location of originals of copied, damaged or modified images within a database of hundreds of thousands of stock images. The method is shown to outperform binary decision tree indexing with invariant frame detection.

With the rapid development of remote sensing platform and sensor technique, the amount of satellite images has frequently increased. In order to analyze, manage and retrieve spatial information, several techniques are used to improve the quality of retrieval systems and to perform semantic in the retrieval process. In this paper, we propose an ontology-based approach for semantic retrieving of satellite images, describing the semantic content image and managing uncertain information. The proposed system is composed of three modules: ontological modeling of scene, ontological models merging and semantic retrieval. The first module, describes the semantic image content by an ontological model based on sensor model, scene model and spatial relations model. The second module develops a reliable ontological model of the satellite scene for merging incompletes models and managing uncertain information and conflict situations. The third module retrieves similar satellite images basing on their ontological models retrieves similar satellites images based on their ontological models. Keywords: Content-based image retrieval, scene interpretation, knowledge representation, ontology merging. 1.