SC-LSH: Une Méthode d'Indexation pour une Recherche de Similarité Approximative dans l'Espace Multidimensionnel

Sanaa Chafik; Imane Daoudi; Mounim A. EL Yacoubi; Hamid El Ouardi

SC-LSH: Une Méthode d'Indexation pour une Recherche de Similarité Approximative dans l'Espace Multidimensionnel

Sanaa Chafik
, Imane Daoudi
, Mounim A. EL Yacoubi
, Hamid El Ouardi

Institut Mines-Télécom
Nancy Université
Hassan II Ain Chock University

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

Résumé

Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest Neighbours search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect search performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. This paper propose a new hashing algorithm to overcome the storage space problem, while keeping a good accuracy and better query time. The Experimental results on a real large-scale dataset show the interest of our approach..

Titre traduit de la contribution	SC-LSH. An indexing method for similarity search in multidimensional space approximate
langue originale	Français
Pages	303-318
Nombre de pages	16
état	Publié - 1 janv. 2015
Modification externe	Oui
Evénement	Conference in Search Infomations and Applications, CORIA 2015 - 12th French Information Retrieval Conference - Paris, France Durée: 18 mars 2015 → 20 mars 2015

Une conférence

Une conférence	Conference in Search Infomations and Applications, CORIA 2015 - 12th French Information Retrieval Conference
Pays/Territoire	France
La ville	Paris
période	18/03/15 → 20/03/15

mots-clés

Content based image retrieval (CBIR)
Curse of dimensionality
LSH
Multidimensional indexing
Nearest neighbour search
Scalability

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

@conference{f9c76ca060344374bec7488209186dc1,

title = "SC-LSH: Une M{\'e}thode d'Indexation pour une Recherche de Similarit{\'e} Approximative dans l'Espace Multidimensionnel",

abstract = "Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest Neighbours search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect search performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. This paper propose a new hashing algorithm to overcome the storage space problem, while keeping a good accuracy and better query time. The Experimental results on a real large-scale dataset show the interest of our approach..",

keywords = "Content based image retrieval (CBIR), Curse of dimensionality, LSH, Multidimensional indexing, Nearest neighbour search, Scalability",

author = "Sanaa Chafik and Imane Daoudi and \{EL Yacoubi\}, \{Mounim A.\} and \{El Ouardi\}, Hamid",

year = "2015",

month = jan,

day = "1",

language = "Fran{\c c}ais",

pages = "303--318",

note = "Conference in Search Infomations and Applications, CORIA 2015 - 12th French Information Retrieval Conference ; Conference date: 18-03-2015 Through 20-03-2015",

}

SC-LSH: Une Méthode d'Indexation pour une Recherche de Similarité Approximative dans l'Espace Multidimensionnel. / Chafik, Sanaa; Daoudi, Imane; EL Yacoubi, Mounim A. et al.
2015. 303-318 Papier présenté � Conference in Search Infomations and Applications, CORIA 2015 - 12th French Information Retrieval Conference, Paris, France.

Résultats de recherche: Contribution à une conférence › Papier › Revue par des pairs

TY - CONF

T1 - SC-LSH

T2 - Conference in Search Infomations and Applications, CORIA 2015 - 12th French Information Retrieval Conference

AU - Chafik, Sanaa

AU - Daoudi, Imane

AU - EL Yacoubi, Mounim A.

AU - El Ouardi, Hamid

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest Neighbours search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect search performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. This paper propose a new hashing algorithm to overcome the storage space problem, while keeping a good accuracy and better query time. The Experimental results on a real large-scale dataset show the interest of our approach..

AB - Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest Neighbours search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect search performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. This paper propose a new hashing algorithm to overcome the storage space problem, while keeping a good accuracy and better query time. The Experimental results on a real large-scale dataset show the interest of our approach..

KW - Content based image retrieval (CBIR)

KW - Curse of dimensionality

KW - LSH

KW - Multidimensional indexing

KW - Nearest neighbour search

KW - Scalability

UR - https://www.scopus.com/pages/publications/84983151072

M3 - Papier

AN - SCOPUS:84983151072

SP - 303

EP - 318

Y2 - 18 March 2015 through 20 March 2015

ER -

SC-LSH: Une Méthode d'Indexation pour une Recherche de Similarité Approximative dans l'Espace Multidimensionnel

Résumé

Une conférence

mots-clés

Autres fichiers et liens

Empreinte digitale

Contient cette citation