A large number of papers [3-12] have been published with the purpose of evaluating the state of the art in the image segmentation domain and trying to provide explanations on what are the best choices for a given of image characteristics.
In  a generic evaluation framework for image segmentation is presented.
Other papers like  try to use voting for removal of noise and other inherent artifacts that results from the segmentation process with efforts going as well into providing specific mathematical models, methods  and probabilistic analysis in order to evaluate the relative quality of segmentation .
This served as a starting point for the implementation of the voting image segmentation algorithm proposed by this paper.
Different segmentation algorithms have been designed having qualities and shortcomings and articles like , ,  have been dedicated to studying the different trade-offs that have to be made.
This section briefly describes the individual image segmentation algorithms used in this paper to demonstrate the proposed concepts, with an emphasis on their upsides and downsides in terms of over/under segmentation i.
In a new article entitled "Rediscovering Market Segmentation" in the February 2006 Harvard Business Review, Meer teams up with marketing research pioneer Daniel Yankelovich to tackle the challenge of how to fix segmentation so it can fulfill its earlier promise.
Yankelovich introduced the idea of nondemographic segmentation in the pages of HBR four decades ago.
Non-demographic segmentation began as a way to focus on the differences among customers that matter the most strategically," says Yankelovich, noted for his innovative work in forecasting social trends.
To halt the drift from its original purpose and power, Yankelovich and Meer suggest six guidelines for ensuring that segmentation inform strategic decisions on such issues as product innovation, pricing and choice of distribution channels.