Cédric Wemmert

An evolutionary approach for ontology driven image interpretation

Image mining and interpretation is a quite complex process. In this article, we propose to model expert knowledge on objects present in an image through an ontology. This ontology will be used to drive a segmentation process by an evolutionary approach. This method uses a genetic algorithm to find segmentation parameters which allow to identify in the image the objects described by the expert in the ontology. The fitness function of the genetic algorithm uses the ontology to evaluate the segmentation. This approach does not needs examples and enables to reduce the semantic gap between automatic interpretation of images and expert knowledge.

Collaborative multi-step mono-level multi-strategy classification

This article deals with the description of a new way to learn from multiple and heterogeneous data sets, and with the integration of this method in a multi-agent hybrid learning system. This system integrates different kinds of unsupervised classification methods and gives a set of clusterings as the result and a unifying result, representing all the other one. In this new approach, the method occurrences compare their results and automatically refine them to try to make them converge towards a unique clustering that unifies all the results. Thus, the data are not really merged but the results from their classification are compared and refined according to the results from all the other data sets. This enables to produce a set of classification hierarchies which classes are very similar, although these hierarchies were extracted from different data sets. Then it is easy to build a unifying result from all of them.

Towards a Semi-automatic Semantic Approach for Satellite Image Analysis☆☆☆

Abstract The extended use of high and very high spatial resolution imagery inherently demands the adoption of classification methods capable of capturing the underlying semantic. Object-oriented classification methods are currently considered the most appropriate alternative, due to the incorporation of contextual information and domain knowledge into the analysis. Integrating knowledge initially requires a detailed process of acquisition and later the achievement of a formal representation. Ontologies constitute a very suitable approach to address both knowledge formalization and exploitation. A novel semi-automatic semantic approach focused on the extraction and classification of urban objects is hereby introduced. The use of a three-layered architecture allows the separation of concerns among knowledge, rules and experience. Knowledge represents the fundamental layer with which the other layers interact. Rules are meant to derive conclusions and make assertions based on knowledge. Finally, the experience layer supports the classification process in case of failure when attempting to identify an object, by applying specific expert rules to infer unusual membership.

Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis

In this chapter, the use of a collaborative multi-strategy clustering method, applied to image analysis, is presented. This method integrates different kinds of clustering algorithms and produces, for each algorithm, a result built according to the results of all the other methods: each method tries to make its result to converge towards the results of the other methods by using consensus operators. This chapter highlights how clustering methods can collaborate and presents results in the paradigm of object-oriented classification of a very high resolution remotely sensed images of an urban area.

On Combining Unsupervised Classification and Ontology Knowledge

This paper presents a way to combine knowledge obtained from a clustering algorithm and from an ontology. Using the both sources of information allows to improve the results of the knowledge discovery process. The basic property of clustering algorithms, which is to group similar objects, is the key of this approach. We use it to extend the knowledge given by an ontology. Indeed, this knowledge can be partial or not enough accurate, and clustering can then be used to fill this lack of information. We also present results and validation in the field of remote sensing image interpretation.

A Fuzzy-Rule Based Ontology for Urban Object Recognition

In this paper we outline the principles of a methodology for semi-automatic recognition of urban objects from satellite images. The methodology aims to provide a framework for bridging the semantic gap problem. Its principle consists in linking abstract geographical domain concepts with image segments, by the means of ontologies use. The imprecision of image data and of qualitative rules formulated by experts geographers are handled by fuzzy logic mechanisms. We have defined fuzzy rules, implemented in SWRL (Semantic Web Rule Language), which allow classification of image segments in the ontology. We propose some fuzzy classification strategies, which are compared and evaluated through an experimentation performed on a VHR image of Strasbourg region.