Paper Info
Title
A FWCL-based method for visual vocabulary formation.
Abstract
Traditional BOV (bag-of-visterms) constructing methods ignore different visual words’ contributions to image representation. Moreover, the size of visual vocabulary is generally set according to experience. Frequent weighted concept lattice (FWCL) is an interesting version of the concept lattice, and helps realize knowledge extraction in a more efficient way. In this paper, we present a dynamic refinement method for visual vocabulary formation based on frequency weighted concept lattice (FWCL) by using its characteristic of hierarchical data analysis and reduction. The original visual words’ weight value is assigned with information entropy, and FWCLs of each semantic category are constructed. According to the extent thresholds, each category of visual vocabulary is built and combined to generate the reduced global visual vocabulary for image representation. By adjusting the extent thresholds, different sizes of reduced visual vocabularies are dynamically extracted from this type of hierarchical structure. Lastly, experiments are carried on the two commonly used datasets, and experimental results show the effectiveness of our method on the scene classification.
Year
DOI
Venue
2016
10.1007/s11042-014-2313-7
Multimedia Tools and Applications
Keywords
Field
DocType
BOV(bag-of-visterms), Visual vocabulary, Frequency weighted concept lattice (FWCL), Scene classification
Computer vision,Pattern recognition,Computer science,Image representation,Visual vocabularies,Artificial intelligence,Natural language processing,Knowledge extraction,Entropy (information theory),Vocabulary,Hierarchical database model,Visual Word
Journal
Volume
Issue
ISSN
75
1
1573-7721
Citations 
PageRank 
References 
0
0.34
34
Authors
5
Name
Order
Citations
PageRank
Sulan Zhang1526.86
Jifu Zhang29519.42
Ping Guo360185.05
Meng Chu400.34
Kai H. Chang511118.19