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A sensibility study of the autobinomial model estimation methods based on a feature similarity index

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Abstract

The estimation of parameters in the Autobinomial model is an important task for characterizing the content of an image and generating synthetic textures. This paper compares the performance of three estimation methods of the model: coding, maximum pseudo-likelihood and conditional least squares, under textures with different levels of additive contamination, using a feature similarity image index, via Monte Carlo studies. This novel framework quantifies the similarity between the original texture and its texture regenerated by each method. Differences in performance were tested with a Repeated Measures ANOVA model design. Simulation results show that the Conditional Least Squares method is associated with the highest value of the similarity image measure in contaminated textures, while Coding and Maximum Pseudo-Likelihood methods have a comparable behavior and there is no clear pattern whether to prefer one over the other. An application for landscape classification using real Landsat images with different spatial resolutions is described.

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Acknowledgments

This paper has been partially supported by SGCyT-UNS and SeCyT-UNC. The imagery used in the application section was kindly provided by CONAE, Argentina. The authors want to thank the Editor, Associate Editor and the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper.

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Authors and Affiliations

  1. Departamento de Matemática, Universidad Nacional del Sur, Bahía Blanca, Argentina

    Silvina Pistonesi & Jorge Martinez

  2. Facultad de Matemática Astronomía y Física (FaMAF), Universidad Nacional de Córdoba, Córdoba, Argentina

    Silvia M. Ojeda

Authors
  1. Silvina Pistonesi
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  2. Jorge Martinez
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  3. Silvia M. Ojeda
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Correspondence to Silvina Pistonesi.

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Pistonesi, S., Martinez, J. & Ojeda, S.M. A sensibility study of the autobinomial model estimation methods based on a feature similarity index. Comput Stat 31, 1327–1357 (2016). https://doi.org/10.1007/s00180-015-0634-2

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  • DOI: https://doi.org/10.1007/s00180-015-0634-2

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