Open Access
An Unsupervised Image Clustering Method Based on EEMD Image Histogram.
Stelios Krinidis,Michail Krinidis,Vassilios Chatzis +2 more
- Vol. 3, pp 151-163
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TLDR
The proposed algorithm exploits an intermediate step derived from the empirical mode decomposition, which can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs).Abstract:
This paper presents a novel unsupervised image clustering approach based on the image histogram, which is processed by the empirical mode decomposition (EMD). The proposed algorithm exploits an intermediate step derived from the empirical mode decomposition, which can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). The IMFs of the image histogram have interesting characteristics and provide a novel workspace that is utilized in order to automatically detect the different clusters into the image under examination. The proposed method was applied to several real and synthetic images and the obtained results show good image clustering robustness.read more
Citations
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Automatic presentation of slide design suggestions
Christopher Michael Maloney,John R. Schilling,Jonathan Chung,Andrew Kunz,Sharena Pari-Monasch,Jonathan Ko,Onur Onder +6 more
TL;DR: In this paper, a method and system for organizing slides by analyzing content and offering alternative solutions for placing content on the slides in an effective and visually interesting organization is provided, which assists in quickly and easily creating presentations that effectively utilize slide space.
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Improving the unsupervised LBG clustering algorithm performance in image segmentation using principal component analysis
TL;DR: The proficiency of eigenvalue-based LBG (E-LBG) algorithm is tested against other clustering algorithm such as Fuzzy $$c$$c-Means and Gustafson–Kessel and acceptable results are obtained.
J-MEANS: A New Local Search Heuristic for Minimum Sum-of-Squares Clustering
Pierre Hansen,Nenad Mladenović +1 more
TL;DR: In this paper, a new local search heuristic, called J-M eans, is proposed for solving the minimum sum of squares clustering problem, where the neighborhood of the current solution is defined by all possible centroid-to-entity relocations followed by corresponding changes of assignments, until a local optimum is reached.
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Image analysis based color suggestions
TL;DR: A non- limiting example is a method for generating suggestions for arranging content based on matching colors in an image as mentioned in this paper, where the suggested design element matches one of the identified candidate colors in the image.
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Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging
TL;DR: The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement and suggests that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.
References
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Proceedings ArticleDOI
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