Extensive research in color naming and color categorization has been more focused on a small number of consensual color categories than towards the development of more subtle color identifications. The work we present in this paper describes an online color-naming model. In this context we evaluated the performance of two probabilistic estimation algorithms for automatic assignment of CIELAB coordinates into an arbitrary number of color names. The algorithms were tested on data gathered in a sophisticated online color naming experiment detailed elsewhere and summarized here. Our methodology resulted in practical color naming models that can support natural language image segmentation, with a computational simplicity that makes them suitable for online applications.