Typical processes of image representation comprehend initial region segmentation followed by a description of single regions’ feature and their relationships. Then a graph model can be exploited in order to integrate the knowledge of the specific regions (that are the attributed relational graph’s (ARG) nodes) and the regions’ relations (that are the ARG’s edges). In this work we use color features to guide region segmentation, geometric features to characterize regions one by one and topological features (and in particular inclusion) to describe regions’ relationships. Guided by the inclusion property we define the Topological Tree (TT) as an image representation model that exploiting the transitive property of inclusion, uses the adjacency and inclusion topological features. We propose an approach based on a recursive version of fuzzy c-means to construct the topological tree directly from the initial image, performing both segmentation and TT construction. The TT can be exploited in many applications of image analysis and image retrieval by similarity in those contexts where inclusion is a key feature: we propose an applicative case of analysis of dermatological images to support the melanoma diagnosis.In this paper describe details of the TT algorithm, including the management of not ideality and an approximate measure of tree similarity in order to retrieve skin lesion with a similar TT-based description.
|Data di pubblicazione:||2003|
|Titolo:||Image Representation and Retrieval with Topological Trees|
|Autori:||C. Grana; G. Pellacani; S. Seidenari; R. Cucchiara|
|Data del convegno:||Jun 9-10|
|Nome del convegno:||Image: E-Learning, Understanding, Information Retrieval and Medical|
|Luogo del convegno:||Cagliari, Italy|
|Titolo del libro:||Image: E-Learning, Understanding, Information Retrieval and Medical Proceedings of the First International Workshop|
|Appare nelle tipologie:||Relazione in Atti di Convegno|
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