Visual database engines are usually based on predefined criteria for retrieving the images in response to a given query.In this paper,we propose a novel approach based on neural networks by which the retrieval criterion is derived on the basis of learning from examples. In particular, the proposed approach uses a graph-based image representation that denotes the relationships among regions in the image and on recursive neural networks which can process directed ordered acyclic graphs. The graph-based representation combines structural and sub-symbolic features of the image, while recursive neural networks can discover the optimal representation for searching the image database. A set of preliminary experiments on artificial images clearly indicate that the proposed approach is very promising.
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|Titolo:||Similarity learning for graph-based image representations|
|Rivista:||PATTERN RECOGNITION LETTERS|
|Citazione:||DE MAURO, C., Diligenti, M., Gori, M., & Maggini, M. (2003). Similarity learning for graph-based image representations. PATTERN RECOGNITION LETTERS, 24(8), 1115-1122.|
|Appare nelle tipologie:||1.1 Articolo in rivista|