The structural image similarity index (SSIM), introduced by Wang and Bovik (IEEE Signal Processing Letters 9-3, pp.
81-84, 2002) measures the similarity between images in terms of luminance, contrast en structure. It has successfully
been deployed to model human visual perception of image distortions and modifications in a wide range of different
imaging applications. Chang and Zhang (Infrared Physics & Technology 51-2, pp. 83-90, 2007) recently introduced the
target structural similarity (TSSIM) clutter metric, which deploys the SSIM to quantify the similarity of a target to its
background in terms of luminance, contrast en structure. They showed that the TSSIM correlates significantly with mean
search time and detection probability. However, it is not immediately obvious to what extent each of the three TSSIM
components contributes to this correlation. Here we evaluate the TSSIM by deploying it to a set of natural images for
which human visual search data are available: the Search_2 dataset. By analyzing the predictive performance of each of
the three TSSIM components, we find that it is predominantly the structural similarity component which determines
human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with
human performance. Since the structural similarity component of the TSSIM is equivalent to a matched filter, it appears
that matched filtering predicts human visual performance when searching for a known target.