Fast Large-Scale Multimedia Indexing and Searching

被引：0

作者：

Mohamed, Hisham ^{[1
]}

Osipyan, Hasmik ^{[1
]}

Marchand-Maillet, Stephane ^{[1
]}

机构：

[1] Univ Geneva, Dept Comp Sci, Viper Grp, Geneva, Switzerland

来源：

2014 12TH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI) | 2014年

关键词：

NEAREST-NEIGHBOR;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Searching for digital images in large-scale multimedia database is a hard problem due to the rapid increase of the digital assets. Metric Permutation Table is an efficient data structure for large-scale multimedia indexing. This data structure is based on the Permutation-based indexing, that aims to predict the proximity between elements encoding their location with respect to their surrounding. The main constraint of the Metric Permutation Table is the indexing time. With the exponential increase of multimedia data, parallel computation is needed. Opening the GPUs to general purpose computation allows to perform parallel computation on a powerful platform. In this paper, we propose efficient indexing and searching algorithms for the Metric Permutation Table using GPU and multi-core CPU. We study the performance and efficiency of our algorithms on large-scale datasets of millions of images. Experimental results show a decrease of the indexing time while preserving the quality of the results.

引用

页数：6

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