Incremental indexing with binary feature based Tversky index using black hole entropic fuzzy clustering in cloud computing

Bel, Koen; Sam, I. Shatheesh

doi:10.1007/s11042-022-12699-1

Multimed Tools Appl

2022

DOI: 10.1007/s11042-022-12699-1

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Incremental indexing with binary feature based Tversky index using black hole entropic fuzzy clustering in cloud computing

Koen Bel

I. Shatheesh Sam

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2024

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Cited by 1 publication

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Image retrieval based on auto-encoder and clustering with centroid update

Nalini Sujantha Bel,

Sam

2024

The Computer Journal

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This paper suggests a revolutionary deep learning method using a dynamic deep auto-encoder for improving the performance of indexing the feature vectors of images by centroid updation. Feature vectors such as color, semantic, and spatial local binary pattern are extracted from the images for content-based image retrieval. The owner encrypts the images for protection using elliptic curve cryptography before uploading them to the cloud. A black hole entropic fuzzy clustering with Tversky indexing is used to retrieve similar information. When the new training image is matched with any of the centroid then the centroid gets updated by using dynamic deep auto-encoder. During the auto-encoder phase, the conflicted data points are dedicated to reconstruction and the reliable data points are helpful to centroid updation. The suggested BHE fuzzy clustering with dynamic deep auto-encoder approach fared better than the current methods, achieving the best accuracy of 97.605%, the highest $\boldsymbol{F_{1}}$ score of 90.210%, better precision of 90.001%, and the highest recall of 95.149%.

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Image retrieval based on auto-encoder and clustering with centroid update

Nalini Sujantha Bel,

Sam

2024

The Computer Journal

View full text Add to dashboard Cite

show abstract

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Incremental indexing with binary feature based Tversky index using black hole entropic fuzzy clustering in cloud computing

Cited by 1 publication

References 42 publications

Image retrieval based on auto-encoder and clustering with centroid update

Image retrieval based on auto-encoder and clustering with centroid update

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