Traditional generative Markov random fields for segmenting images model the image data and corresponding labels jointly, which requires extensive independence assumptions for tractability. We present the conditional random field for an application in sign detection, using typical scale and orientation selective texture filters and a nonlinear texture operator based on the grating cell. The resulting model captures dependencies between neighboring image region labels in a data-dependent way that escapes the difficult problem of modeling image formation, instead focusing effort and computation on the labeling task. We compare the results of training the model with pseudo-likelihood against an approximation of the full likelihood with the iterative tree reparameterization algorithm and demonstrate improvement over previous methods.