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A hybrid shape-based image clustering using time-series analysis

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Abstract

Clustering of different shapes of the same object has an inordinate impact on various domains, including biometrics, medical science, biomedical signal analysis, and forecasting, for the analysis of huge volume of data into different groups. In this work, we present a novel shape-based image clustering approach using time-series analysis, to guarantee the robustness over the conventional clustering techniques. To evaluate the performance of the proposed procedure, we employed a dataset consists of various real-world irregular shaped objects. The shapes of different objects are first extracted from the entire dataset based on similar pattern using mean structural similarity index. Furthermore, we performed radical scan on the extracted shapes for converting them to one-dimensional (1D) time-series data. Finally, the time series are clustered to form subgroups using hierarchical divisive clustering approach with average linkage, and Pearson as distance metrics. A comparative study with other conventional distance metrices was also conducted. The results established the superiority of using Pearson correlation measure, which provided the maximum F1-score with exact number of shapes under a sub-cluster, while the corresponding outcomes of other approaches results in a poor and inappropriate clustering.

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Authors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Atreyee Mondal & Nilanjan Dey

  2. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR

    Simon Fong

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, 31527, Egypt

    Amira S. Ashour

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  1. Atreyee Mondal
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  2. Nilanjan Dey
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  3. Simon Fong
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  4. Amira S. Ashour
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Correspondence to Amira S. Ashour.

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Mondal, A., Dey, N., Fong, S. et al. A hybrid shape-based image clustering using time-series analysis. Multimed Tools Appl 80, 3793–3808 (2021). https://doi.org/10.1007/s11042-020-09765-x

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