Hypergraph-based image retrieval for graph-based representation

被引：18

作者：

Jouili, Salim ^{[1
]}

Tabbone, Salvatore ^{[2
]}

机构：

[1] EURA NOVA, B-1435 Mont St Guibert, Belgium

[2] Univ Lorraine, LORIA, UMR 7503, F-54506 Vandoeuvre Les Nancy, France

来源：

PATTERN RECOGNITION | 2012年 / 45卷 / 11期

关键词：

Graph indexing; Graph retrieval; CBIR; ALGORITHMS;

D O I：

10.1016/j.patcog.2012.04.016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：4054 / 4068

页数：15

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