Image measures for segmentation algorithm evaluation of automatic target recognition system

Image measure is a very important part of automatic target recognition algorithm performance evaluation. Whether the image measure accurately relates with algorithm performance will affect directly evaluations. In this paper current image measures are introduced, and the deficiency of the target to background contrast (TBC) measure is analyzed, which is the best single measure and the representative of current general measures. A new texture-based image clutter measure (TIC) is derived from gray level cooccurrence (GLC) matrices, which embody important texture information. The result of testing two measures TBC and TIC shows that the relation between TBC and segmentation algorithm performance is monotonic rising in given scenario condition, but in complex condition it will fail, and that in both conditions TIC has very good monotonic relation with the segmentation algorithm performance. TIC is a fairly robust indicator of segmentation algorithm performance and better suited than TBC