Nowadays, due to the spread of digital imaging technologies, the design of effective content based image retrieval (CBIR) systems is perceived by the research community as a primary problem. Various techniques such as clustering and relevance feedback were proposed to obtain a certain level of knowledge about a given image database. Often clustering techniques were used to obtain a first level characterization of the image database used to speed up the successive stage of queries. In this work the authors use the knowledge obtained using a fuzzy clustering algorithm to reinforce the user feedback. The system was tested on the Columbia Coil-20 image database and the obtained results seem to be encouraging
A fuzzy logic based approach to feedback reinforcement in image retrieval / DI LECCE, Vincenzo; Amato, A.. - 5754:(2009), pp. 939-947. (Intervento presentato al convegno 5th International Conference on Intelligent Computing, ICIC 2009 tenutosi a Ulsan, South Korea nel September 16-19, 2009) [10.1007/978-3-642-04070-2_99].
A fuzzy logic based approach to feedback reinforcement in image retrieval
DI LECCE, Vincenzo;
2009-01-01
Abstract
Nowadays, due to the spread of digital imaging technologies, the design of effective content based image retrieval (CBIR) systems is perceived by the research community as a primary problem. Various techniques such as clustering and relevance feedback were proposed to obtain a certain level of knowledge about a given image database. Often clustering techniques were used to obtain a first level characterization of the image database used to speed up the successive stage of queries. In this work the authors use the knowledge obtained using a fuzzy clustering algorithm to reinforce the user feedback. The system was tested on the Columbia Coil-20 image database and the obtained results seem to be encouragingI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
