Mining of Images by K-Medoid Clustering Using Content Based Descriptors

Jayaswal, Ruchi; Jha, Jaimala; Dixit, Manish

doi:10.14257/ijsip.2017.10.8.12

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The efficient clustering algorithm for probability density functions based on 𝑘-medoids [21]. It also has a high cluster accuracy compared to other distance-based partitioning algorithms for mixed variable data [22]. The PAM algorithm is used to reduce computational time at the swap step [25].…”

Section: Research Backgroundmentioning

confidence: 99%

Pattern Based Image Retrieval System by Using Clustering Techniques

Dharani¹,

Kumari²,

Sindhupiriya³

et al. 2023

Data Analytics and Artificial Intelligence

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Nowadays, the image database is growing in enormous size with heterogeneous categories. Similarly, there is rising demands from users in various ways. The most demanding domains in society are health care, Agriculture, commerce, and security. Healthcare domain is concerned with diagnosing the disease. Security domain is involved in investigation of the Criminals. Commerce domain needs analysis to recognize the right product. Agriculture domain requires processing of disease affected fruit images. The PBIR system that combines evidence from multiple digital image domains can reduce those problems of existing image retrieval systems. The PBIR system is used to find uncertain parts of the image during augmentation steps. The result is identified with various parameters (e.g., accuracy, precision, distance, sum of distance), showing that the performance of the k-medoids pam algorithm better than the existing iterative clustering algorithms

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Section: Research Backgroundmentioning

confidence: 99%

Pattern Based Image Retrieval System by Using Clustering Techniques

Dharani¹,

Kumari²,

Sindhupiriya³

et al. 2023

Data Analytics and Artificial Intelligence

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Transfer learning-based Fashion Image Classification using Hybrid 2D-CNN and ImageNet Neural Network

Duseja¹

2021

IJRASET

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Many algorithms have been developed as a result of recent advances in machine learning to handle a variety of challenges. In recent years, the most popular transfer learning method has allowed researchers and engineers to run experiments with minimal computing and time resources. To tackle the challenges of classification, product identification, product suggestion, and picture-based search, this research proposed a transfer learning strategy for Fashion image classification based on hybrid 2D-CNN pretrained by VGG-16 and AlexNet. Pre-processing, feature extraction, and classification are the three parts of the proposed system's implementation. We used the Fashion MNIST dataset, which consists of 50,000 fashion photos that have been classified. Training and validation datasets have been separated. In comparison to other conventional methodologies, the suggested transfer learning approach has higher training and validation accuracy and reduced loss. Keywords: Machine Learning, Transfer Learning, Convolutional Neural Network, Image Classification, VGG16, AlexNet, 2D CNN.

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Overview of Different Methods Used in Clustering Algorithms

Yousefi

Odabas

Oktaş

2020

Black Sea Journal of Engineering and Science

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Özet Veri madenciliği, birçok teknik ve algoritmayı kullanarak büyük veri tabanlarından anlamlı bilgileri çıkarma işlemidir. Veri madenciliği genellikle, "verilerde bilgi keşfi" olarak adlandırılan ve bu bilgileri bulmak için kullanılan yöntemlerdir. Veri madenciliğinin temel yöntemlerinden birisi olan kümeleme yöntemidir. Kümeleme yöntemi günümüz dünyasında hızla çoğalan verilerin analizinde kullanılacak en güçlü yöntemlerdendir. Kümeleme bazı benzerlik mesafelerine dayalı olarak verilerdeki doğal gruplamaları veya kümeleri bulma tekniğidir. Kümeleme aslında birçok farklı veri analizlerinde temel bir adımdır. Bundan dolayı bu derlemede kümeleme algoritmalarında kullanılan farklı yöntemler özet bir şekilde anlatılmıştır.

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Mining of Images by K-Medoid Clustering Using Content Based Descriptors

Abstract: Abstract

Cited by 3 publications

References 7 publications

Pattern Based Image Retrieval System by Using Clustering Techniques

Pattern Based Image Retrieval System by Using Clustering Techniques

Transfer learning-based Fashion Image Classification using Hybrid 2D-CNN and ImageNet Neural Network

Overview of Different Methods Used in Clustering Algorithms

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