Structural similarity determines search time and detection probability

The recently introduced TSSIM clutter metric is currently the best predictor of human visual search performance for natural images (Chang and Zhang [1]). The TSSIM quantifies the similarity of a target to its background in terms luminance, contrast and structure. It correlates stronger with experimental mean search times and detection probabilities than other clutter metrics (Chang and Zhang [1] and [2]). Here we show that it is predominantly the structural similarity component of the TSSIM which determines human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with human performance. This result agrees with previous reports that human observers mainly rely on structural features to recognize image content. 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.

Research highlights
► The structural similarity component of the TSSIM clutter metric determines human visual search performance.
► The luminance and contrast components of the TSSIM show no relation with human performance.
► Matched filtering predicts human visual performance when searching for a known target.