BARTOŇ, Stanislav, Vlastislav DOHNAL, Jan SEDMIDUBSKÝ a Pavel ZEZULA. Building Self-Organized Image Retrieval Network. Online. In Proceeding of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval (LSDS-IR'08). USA: ACM New York, 2008. s. 51-58. ISBN 978-1-60558-254-2. [citováno 2024-04-23]
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Základní údaje
Originální název Building Self-Organized Image Retrieval Network
Název česky Building Self-Organized Image Retrieval Network
Autoři BARTOŇ, Stanislav (203 Česká republika, garant), Vlastislav DOHNAL (203 Česká republika), Jan SEDMIDUBSKÝ (203 Česká republika) a Pavel ZEZULA (203 Česká republika)
Vydání USA, Proceeding of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval (LSDS-IR'08), od s. 51-58, 8 s. 2008.
Nakladatel ACM New York
Další údaje
Originální jazyk angličtina
Typ výsledku Stať ve sborníku
Obor 10201 Computer sciences, information science, bioinformatics
Stát vydavatele Spojené státy
Utajení není předmětem státního či obchodního tajemství
Kód RIV RIV/00216224:14330/08:00024257
Organizační jednotka Fakulta informatiky
ISBN 978-1-60558-254-2
Klíčová slova anglicky content-based image retrieval; peer-to-peer network; self-organized system; social networking
Štítky content-based image retrieval, DISA, peer-to-peer network, self-organized system, social networking
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnil: RNDr. Stanislav Bartoň, Ph.D., učo 608. Změněno: 12. 11. 2008 13:24.
Anotace
We propose a self-organized content-based Image Retrieval Network (IRN) that is inspired by a Metric Social Network (MSN) search system. The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries. Thus a shared database where each peer is fully in charge of its data, is created. The self-organization of the network is obtained by exploiting the social-network approach of the MSN -- the connections between peers in the network are created as social-network relationships formed on the basis of a query-answer principle. The knowledge of answers to previous queries is used to fast navigate to peers, possibly containing the best answers to new queries. Additionally, the network uses a randomized mechanism to explore new and unvisited parts of the network. In this way, the self-adaptability and robustness of the system are achieved. The proposed concepts are verified using a real network consisting of 2,000 peers containing descriptive features of 10 million images from the Flickr Photo Sharing system.
Anotace česky
We propose a self-organized content-based Image Retrieval Network (IRN) that is inspired by a Metric Social Network (MSN) search system. The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries. Thus a shared database where each peer is fully in charge of its data, is created. The self-organization of the network is obtained by exploiting the social-network approach of the MSN -- the connections between peers in the network are created as social-network relationships formed on the basis of a query-answer principle. The knowledge of answers to previous queries is used to fast navigate to peers, possibly containing the best answers to new queries. Additionally, the network uses a randomized mechanism to explore new and unvisited parts of the network. In this way, the self-adaptability and robustness of the system are achieved. The proposed concepts are verified using a real network consisting of 2,000 peers containing descriptive features of 10 million images from the Flickr Photo Sharing system.
Návaznosti
GP201/07/P240, projekt VaVNázev: Distribuované indexační struktury pro podobnostní hledání
Investor: Grantová agentura ČR, Distribuované indexační struktury pro podobnostní hledání
1ET100300419, projekt VaVNázev: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
Investor: Akademie věd ČR, Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
VytisknoutZobrazeno: 23. 4. 2024 15:08