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Clustering Model Based on the Human Visual System

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Recent Metaheuristic Computation Schemes in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 948))

Abstract

Clustering involves the process of dividing a collection of abstract objects into a number of groups, which are integrated with similar elements. Several clustering methods have been introduced and developed in the literature with different performance levels. Among such approaches, density algorithms present the best advantages, since they are able to find clusters from a dataset under different scales, shapes, and densities without requiring the number of groups as input. On the other hand, there are processes that humans perform much better than deterministic approaches or computers. In fact, humans can visually cluster data exceptionally well without the necessity of any training. Under this unique capability, clustering, instantaneous, and effortless for humans, represents a fundamental challenge for artificial intelligence. In this chapter, a simple clustering model inspired by the way in which the human visual system associates patterns spatially is presented. The model, at some abstraction level, can be characterized as a density grouping strategy. The approach is based on Cellular Neural Networks (CNNs), which have demonstrated to be the best models for emulating the human visual system. In the method, similar to the biological model, CNN is used to build especially groups while an automatic mechanism tries different resolution scales to find the best possible data categorization. Different datasets have been adopted to evaluate the performance of the algorithm. Their results are also compared with popular density clustering techniques from the literature. Computational results demonstrate that the CNN approach presents competitive results in comparison with other algorithms regarding accuracy and robustness.

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Author information

Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas & Alma Rodríguez

  2. Facultad de Ingeniería, Universidad Panamericana, Zapopan, Jalisco, Mexico

    Alma Rodríguez, Avelina Alejo-Reyes & Carolina Del-Valle-Soto

  3. Centro de Eneñanza Técnica Industrial, Universidad de Guadalajara, Guadalajara, Mexico

    Alma Rodríguez

Authors
  1. Erik Cuevas
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  2. Alma Rodríguez
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  3. Avelina Alejo-Reyes
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  4. Carolina Del-Valle-Soto
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Correspondence to Erik Cuevas .

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Cuevas, E., Rodríguez, A., Alejo-Reyes, A., Del-Valle-Soto, C. (2021). Clustering Model Based on the Human Visual System. In: Recent Metaheuristic Computation Schemes in Engineering. Studies in Computational Intelligence, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-66007-9_6

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