A Fusion of Bag of Word Model and Hierarchical K-Means++ in Image Retrieval View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

My Kieu , Khai Dinh Lai , Tam Duc Tran , Thai Hoang Le

ABSTRACT

This paper proposes an Image Retrieval method using the Bag of Word model combining with Hierarchical K-Means++ algorithm to store and arrange databases, called BoW-HKM++ approach. A Hierarchical tree is proposed to improve the processing speed in retrieval data. In this research: (1) we first build a bag of word model based on SIFT feature extraction on images; (2) then show its improvement by using inverted index technical for whole feature databases; (3) the Hierarchical K-Means++ algorithm is also proposed in the bag of word model to optimize retrieval system. Experiment results were evaluated with k-fold cross-validation method on 2 datasets of James Z. Wang Research Group. Finally, the obtained results will be compared with other method and discussed to prove the effectiveness of proposed framework in Image Retrieval problems. More... »

PAGES

397-408

Book

TITLE

Integrated Uncertainty in Knowledge Modelling and Decision Making

ISBN

978-3-319-49045-8
978-3-319-49046-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-49046-5_34

DOI

http://dx.doi.org/10.1007/978-3-319-49046-5_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084908041


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