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The increasing availability of High Spatial Resolution (HSR) satellite images is an opportunity to characterize and identify urban objects. Thus, the augmentation of the precision led to a need of new image analysis methods using region-based (or object-based) approaches. In this field, an important challenge is the use of domain knowledge for automatic urban objects identification, and a major issue is the formalization and exploitation of this knowledge. In this paper, we present the building steps of a knowledge-base of urban objects allowing to perform the interpretation of HSR images in order to help urban planners to automatically map the territory. The knowledge-base is used to assign segmented regions (i.e. extracted from the images) into semantic objects (i.e. concepts of the knowledge-base). A matching process between the regions and the concepts of the knowledge-base is proposed, allowing to bridge the semantic gap between the images content and the interpretation. The method is validated on Quickbird images of the urban areas of Strasbourg and Marseille (France). The results highlight the capacity of the method to automatically identify urban objects using the domain knowledge.

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Abstract-Although agreement between annotators who mark feature locations within images has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of foreground-background segmentation algorithms. Many researchers utilise ground truth in experimentation and more often than not this ground truth is derived from one annotator's opinion. How does the difference in opinion affect an algorithm's evaluation? A methodology is applied to four image processing problems to quantify the inter-annotator variance and to offer insight into the mechanisms behind agreement and the use of ground truth. It is found that when detecting linear structures annotator agreement is very low. The agreement in a structure's position can be partially explained through basic image properties. Automatic segmentation algorithms are compared to annotator agreement and it is found that there is a clear relation between the two. Several ground truth estimation methods are used to infer a number of algorithm performances. It is found that: the rank of a detector is highly dependent upon the method used to form the ground truth; and that although STAPLE and LSML appear to represent the mean of the performance measured using individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted combination methods-consensus votingaccentuates more obvious features, resulting in an overestimation of performance. It is concluded that in some datasets it is not possible to confidently infer an algorithm ranking when evaluating upon one ground truth.

Highlights• A didactic presentation of issues raised by clustering is given.• An up-to-date review and classi cation of collaborative clustering methods is presented.• The questions about why, when and how collaborative clustering can help are addressed. AbstractClustering is one type of unsupervised learning where the goal is to partition the set of objects into groups called clusters. Faced to the difficulty to design a general purpose clustering algorithm and to choose a good, let alone perfect, set of criteria for clustering a data set, one solution is to resort to a variety of clustering procedures based on different techniques, parameters and/or initializations, in order to construct one (or several) final clustering(s). The hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution.In the cooperative clustering model, as Ensemble Clustering, a set of clustering algorithms are used in parallel on a given data set: the local results are combined to get an hopefully better overall clustering. In the collaborative framework, the goal is that each local computation, quite possibly applied to distinct data sets, benefit from the work done by the other collaborators. This paper is dedicated to collaborative clustering. In particular, after a brief overview of clustering and the major issues linked to, it presents main challenges related to organize and control the collaborative process.

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