Evaluating Image Recognition Efficiency using a Self-Organizing Map

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Author(s)

Rui M. Ligeiro 1,* Andrei B. Utkin 1

1. INOV INESC INOVAÇÃO, Rua Alves Redol 9, 1000-029 Lisbon, Portugal

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2016.07.01

Received: 10 Mar. 2016 / Revised: 9 Apr. 2016 / Accepted: 11 May 2016 / Published: 8 Jul. 2016

Index Terms

Image recognition, Unsupervised learning, Self-Organizing Maps, Hierarchical clustering

Abstract

Recognition and classification of images is an extremely topical interdisciplinary area that covers image processing, computer graphics, artificial intelligence, computer vision, and pattern recognition, resulting in many applications based on contemporary mobile devices. Developing reliable recognition schemes is a difficult task to accomplish. It depends on many factors, such as illumination, acquisition quality and the database images, in particular, their diversity. In this paper we study how the data diversity affects decision making in image recognition, presenting a database driven classification-error predictor. The predictor is based on a hybrid approach that combines a self-organizing map together with a probabilistic logical assertion method. By means of a clustering approach, the model provides fast and efficient assessment of the image database heterogeneity and, as expected, indicates that such heterogeneity is of paramount importance for robust recognition. The practicality of the model is demonstrated using a set of image samples collected from a standard traffic sign database publicly available by the UK Department for Transport.

Cite This Paper

Rui M. Ligeiro, Andrei B. Utkin,"Evaluating Image Recognition Efficiency using a Self-Organizing Map", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.7, pp.1-8, 2016. DOI: 10.5815/ijigsp.2016.07.01

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