BAG OF WORDS FOR LARGE SCALE OBJECT RECOGNITION Properties and Benchmark

被引：0

作者：

Aly, Mohamed ^{[1
]}

Munich, Mario ^{[2
]}

Perona, Pietro ^{[1
]}

机构：

[1] CALTECH, Computat Vis Lab, Pasadena, CA 91125 USA

[2] Evolut Robot, Pasadena, CA USA

来源：

VISAPP 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS | 2011年

关键词：

Image search; Image retrieval; Bag of words; Inverted file; Min hash; Benchmark; Object recognition;

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：

Object Recognition in a large scale collection of images has become an important application of widespread use. In this setting, the goal is to find the matching image in the collection given a probe image containing the same object. In this work we explore the different possible parameters of the bag of words (BoW) approach in terms of their recognition performance and computational cost. We make the following contributions: 1) we provide a comprehensive benchmark of the two leading methods for BoW: inverted file and min-hash; and 2) we explore the effect of the different parameters on their recognition performance and run time, using four diverse real world datasets.

引用

页码：299 / 306

页数：8