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How to use Bag-of-Words model better for image classification
Wang, Chong; Huang, Kaiqi
AbstractThe Bag-of-Words (BoW) framework is well-known in image classification. In the framework, there are two essential steps: 1) coding, which encodes local features by a visual vocabulary, and 2) pooling, which pools over the response of all features into image representation. Many coding and pooling methods are proposed, and how to apply them better in different conditions has become a practical problem. In this paper, to better use BoW in different applications, we study the relation between many typical coding methods and two popular pooling methods. Specifically, complete combinations of coding and pooling are evaluated based on an extremely large range of vocabulary sizes (16 to 1M) on five primary and popular datasets. Three typical ones are 15 Scenes, Caltech 101 and PASCAL VOC 2007, while the other two large-scale ones are Caltech 256 and ImageNet. Based on the systematic evaluation, some interesting conclusions are drawn. Some conclusions are the extensions of previous viewpoints, while some are different but important to understand BoW model. Based on these conclusions, we provide detailed application criterions by evaluating coding and pooling based on precision, efficiency and memory requirements in different applications. We hope that this study can be helpful to evaluate different coding and pooling methods, the conclusions can be beneficial to better understand BoW, and the application criterions can be valuable to use BoW better indifferent applications. (C) 2014 Elsevier B.V. All rights reserved.
KeywordImage Classification Bag-of-words Visual Vocabulary Coding Pooling
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
Indexed BySCI
WOS Research AreaComputer Science ; Engineering ; Optics
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Optics
WOS IDWOS:000356196400006
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Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Wang, Chong,Huang, Kaiqi. How to use Bag-of-Words model better for image classification[J]. IMAGE AND VISION COMPUTING,2015,38(0):65-74.
APA Wang, Chong,&Huang, Kaiqi.(2015).How to use Bag-of-Words model better for image classification.IMAGE AND VISION COMPUTING,38(0),65-74.
MLA Wang, Chong,et al."How to use Bag-of-Words model better for image classification".IMAGE AND VISION COMPUTING 38.0(2015):65-74.
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