Fast Approximate Kernel-Based Similarity Search for Image Retrieval Task

被引：0

作者：

Gorisse, David ^{[1
]}

Cord, Matthieu ^{[2
]}

Precioso, Frederic ^{[1
]}

Philipp-Foliguet, Sylvie ^{[1
]}

机构：

[1] Univ Cergy Pontoise, ENSEA, ETIS, CNRS, Cergy Pontoise, France

[2] UPMC P6, LIP6, Paris, France

来源：

19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 | 2008年

关键词：

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：

In content based image retrieval, the success of an), distance-based indexing scheme depends critically on the quality of the chosen distance metric. We propose in this paper a kernel-based similarity approach working on sets of vectors to represent images. We introduce a method for fast approximate similarity search in large image databases with our kernel-based similarity metric. We evaluate our algorithm on image retrieval task and show it to be accurate and faster than linear scanning.

引用

页码：3390 / 3393

页数：4

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