An efficient method for content based image retrieval using histogram graph

Quynh, Nguyen Huu; Tao, Ngo Quoc; Giang, Ngo Truong

doi:10.1109/icarcv.2008.4795632

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2016

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quynh,Tao and Gyang [9] gives a method for similarity major between image is proposed called Histogram Graph (HISG) .Image are divided in the sequence of sectors and calculate histogram for each color where each bin represented by vertex of the graph. Sum of the minimum cost matching is the distance between two images.…”

Section: Related Workmentioning

confidence: 99%

Content based Image Retrieval using Clustering

Raikwar¹,

Jain²

2012

IJCA

View full text Add to dashboard Cite

To retrieve appropriate information from large image datasets, Content Based image retrieval (CBIR) is a popular approach. In this paper we use binary clustering simultaneously on target and query images to retrieve color difference. One also measure geometric spreadness of each color, using coordinate information of clusters and used it with color difference with some weighted. Experimental results show that CBIRC gives better result as compare to other binning methods and ACE.

show abstract

Section: Related Workmentioning

confidence: 99%

Content based Image Retrieval using Clustering

Raikwar¹,

Jain²

2012

IJCA

View full text Add to dashboard Cite

show abstract

Filtration efficiency and filter resistance of nylon-6 and nylon-6/chitosan nanofibrous membranes

Danwanichakul

Yooyanyong

et al. 2016

View full text Add to dashboard Cite

Introducing New Meta Search Model through Content based Image Retrieval Algorithm

Giri¹,

Sharma²,

Dadheech³

2012

IJCA

View full text Add to dashboard Cite

This paper presents an approach to model content based metasearch engine which search all the content related images present in the dataset. Searching through the keywords in an image database require a lot of Meta data (keywords about the image) to be stored for each image in a separate database. This does not lead to an effective search mechanism. This mechanism basically works for the relevant output for the input query image. The process based on the image extraction by the implemented functions like "edge histogram", "image feature extraction i.e. sharpness, smoothness, color etc.

show abstract

An efficient method for content based image retrieval using histogram graph

Cited by 3 publications

References 15 publications

Content based Image Retrieval using Clustering

Content based Image Retrieval using Clustering

Filtration efficiency and filter resistance of nylon-6 and nylon-6/chitosan nanofibrous membranes

Introducing New Meta Search Model through Content based Image Retrieval Algorithm

Contact Info

Product

Resources

About