An approach to image clustering and retrieval

2018 ◽  
Author(s):  
Izidor Gertner ◽  
Bharat Rosanlall
Keyword(s):  
Image Clustering

User profiles’ image clustering for digital investigations

2021 ◽  
Vol 38 ◽  
pp. 301171
Author(s):  
Rahimeh Rouhi ◽  
Flavio Bertini ◽  
Danilo Montesi
Keyword(s):  
Image Clustering ◽  
User Profiles

Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

2021 ◽  
Vol 216 ◽  
pp. 106814
Author(s):  
Krishna Gopal Dhal ◽  
Arunita Das ◽  
Swarnajit Ray ◽  
Jorge Gálvez
Keyword(s):  
Firefly Algorithm ◽  
Image Clustering ◽  
Fuzzy Image

Factor-Bounded Nonnegative Matrix Factorization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-18
Author(s):  
Kai Liu ◽  
Xiangyu Li ◽  
Zhihui Zhu ◽  
Lodewijk Brand ◽  
Hua Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimization Methods ◽  
Auxiliary Function ◽  
Image Clustering ◽  
Real World Datasets ◽  
The Relationship ◽  
Matrix Factors

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.

Image Clustering via Deep Embedded Dimensionality Reduction and Probability-Based Triplet Loss

2020 ◽  
Vol 29 ◽  
pp. 5652-5661
Author(s):  
Yuanjie Yan ◽  
Hongyan Hao ◽  
Baile Xu ◽  
Jian Zhao ◽  
Furao Shen
Keyword(s):  
Dimensionality Reduction ◽  
Image Clustering ◽  
Triplet Loss
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