A Hybrid Model for Near-Duplicate Image Detection in MapReduce Environment
Clicks: 128
ID: 277551
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
128 views
43 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #6 of 6 articles by views in tem journal
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
It has been proven that the large-scale image dataset is strictly complex in content-based image retrieval (CBIR) as the present strategies in CBIR might have difficulties in processing it. Other than this, near-duplicate images would possibly consume space, in which as an alternative can be used for storing other or unique images. In order to solve these problems, MapReduce has been used for speed-up filtering near-duplicate images. However, there is still a lack of accuracy in detecting near-duplicate images. Hence, this study has discovered that image features extraction by means of Principal Component Analysis (PCA) technique, which is primarily based on the matrix of image representation that will expand the similarity of detection. There is a need whereby PCA approach requires to be enhanced resulting from the lack of the extraction of features in Songket motives images. Therefore, this study proposes a new hybrid model that will integrate PCA with MapReduce for image feature extraction and clustering the large-scale image dataset in the cloud environment. In view of this, the present study employs the use of a qualitative experimental design model and goes through three main phases iteration: firstly, is the analysis and design phase, secondly is a development phase and lastly is testing and evaluation phase. However, this study focuses only on the analysis and design phase. The outcomes process of the empirical phase is followed by designing the algorithm and model according to the result of literature reviews. The expected results of thisstudy is a proposed model and extract principal component elements of the large-scale image dataset using PCA, as well as boosting up time in filtering the images through MapReduce environment.
| Reference Key |
yusof2019atem
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Yusof, Nadiah;Ismail, Amirah;Majid, Nazatul Aini Abd; |
| Journal | tem journal |
| Year | 2019 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.