In content-based image retrieval a major problem is the presence of noisy shapes. It is well known that persistent Betti numbers are a shape descriptor that admits a dissimilarity distance, the matching distance, stable under continuous shape deformations. In this paper we focus on the problem of dealing with noise that changes the topology of the studied objects. We present a general method to turn persistent Betti numbers into stable descriptors also in the presence of topological changes. Retrieval tests on the Kimia-99 database show the effectiveness of the method.
Persistent Betti Numbers for a Noise Tolerant Shape-Based Approach to Image Retrieval / P. Frosini; C. Landi. - STAMPA. - (2011), pp. 294-301. (Intervento presentato al convegno 14th International Conference on Computer Analysis of Images and Pattern tenutosi a Siviglia, Spagna nel 29-31 Agosto 2011) [10.1007/978-3-642-23672-3_36].
Persistent Betti Numbers for a Noise Tolerant Shape-Based Approach to Image Retrieval
FROSINI, PATRIZIO;LANDI, CLAUDIA
2011
Abstract
In content-based image retrieval a major problem is the presence of noisy shapes. It is well known that persistent Betti numbers are a shape descriptor that admits a dissimilarity distance, the matching distance, stable under continuous shape deformations. In this paper we focus on the problem of dealing with noise that changes the topology of the studied objects. We present a general method to turn persistent Betti numbers into stable descriptors also in the presence of topological changes. Retrieval tests on the Kimia-99 database show the effectiveness of the method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
