Abstract: In this paper, we present two techniques to reveal image features that attract the eye during visual search: the discrimination image paradigm and principal component analysis. In preliminary experiments, we employed these techniques to identify image features used to identify simple targets embedded in 1/ƒ noise. Two main findings emerged. First, the loci of fixations were not random but were driven by local image features, even in very noisy displays. Second, subjects often searched for a component feature of a target rather that the target itself, even if the target was a simple geometric form. Moreover, the particular relevant component varied from individual to individual. Also, principal component analysis of the noise patches at the point of fixation reveals global image features used by the subject in the search task. In addition to providing insight into the human visual system, these techniques have relevance for machine vision as well. The efficacy of a foveated machine vision system largely depends on its ability to actively select 'visually interesting' regions in its environment. The techniques presented in this paper provide valuable low-level criteria for executing human-like scanpaths in such machine vision systems.