Assessing the Distinctiveness and Representativeness of Visual Vocabularies

被引：1

作者：

Chang, Leonardo ^{[1
,2
]}

Perez-Suarez, Airel ^{[1
]}

Rodriguez-Collada, Maximo ^{[1
]}

Hernandez-Palancar, Jose ^{[1
]}

Arias-Estrada, Miguel ^{[2
]}

Enrique Sucar, Luis ^{[2
]}

机构：

[1] Adv Technol Applicat Ctr CENATAV, 7A 21406 Siboney, Havana 12200, Cuba

[2] INAOE, Puebla 72840, Mexico

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2015 | 2015年 / 9423卷

关键词：

Bag of visual words; Visual vocabulary; Object categorization; Object recognition;

D O I：

10.1007/978-3-319-25751-8_40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bag of Visual Words is one of the most widely used approaches for representing images for object categorization; however, it has several drawbacks. In this paper, we propose three properties and their corresponding quantitative evaluation measures to assess the ability of a visual word to represent and discriminate an object class. Additionally, we also introduce two methods for ranking and filtering visual vocabularies and a soft weighting method for BoW image representation. Experiments conducted on the Caltech-101 dataset showed the improvement introduced by our proposals, which obtained the best classification results for the highest compression rates when compared with a state-of-the-art mutual information based method for feature selection.

引用

页码：331 / 338

页数：8

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