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
收藏
页码:331 / 338
页数:8
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