Superpixel methods have become popular in recent years as they provide an efficient preprocessing tool for a manifold of computer vision applications. In this work, we propose a method based on a self-adapting and self-growing network, which is bred starting from two random initialization seeds in the image. Such a network, which is a modification of the Instantaneous Topological Map (ITM), is inspired to a Growing Neural Gas (GNG) and like many other self adapting tools employs a Hebbian learning framework. Key point in competitive learning is the definition of a suitable distance function, which we analyse in depth in this work. Distance is indeed the notion which allows to link unsupervised competitive learning with segmentation, where cluster formation reduces to node creation and adaptation within the exploration of a suitable multidimensional input space.
A generative superpixel method
MORERIO, PIETRO;MARCENARO, LUCIO;REGAZZONI, CARLO
2014-01-01
Abstract
Superpixel methods have become popular in recent years as they provide an efficient preprocessing tool for a manifold of computer vision applications. In this work, we propose a method based on a self-adapting and self-growing network, which is bred starting from two random initialization seeds in the image. Such a network, which is a modification of the Instantaneous Topological Map (ITM), is inspired to a Growing Neural Gas (GNG) and like many other self adapting tools employs a Hebbian learning framework. Key point in competitive learning is the definition of a suitable distance function, which we analyse in depth in this work. Distance is indeed the notion which allows to link unsupervised competitive learning with segmentation, where cluster formation reduces to node creation and adaptation within the exploration of a suitable multidimensional input space.
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