A major challenge facing content-based image retrieval is bridging the gap between low-level image primitives and high-level semantics. We have proposed a new approach for semantic image classification that utilizes the adaptive perceptual color-texture segmentation algorithm by Chen et al., which segments natural scenes into perceptually uniform regions. The color composition and spatial texture features of the regions are used as medium level descriptors, based on which the segments are classified into semantic categories. The segment features consist of spatial texture orientation information and color composition in terms of a limited number of spatially adapted dominant colors. The feature selection and the performance of the classification algorithms are based on the segment statistics. We investigate the dependence of the segment statistics on the segmentation algorithm. For this, we compare the statistics of the segment features obtained using the Chen et al. algorithm to those that correspond to human segmentations, and show that they are remarkably similar. We also show that when human segmentations are used instead of the automatically detected segments, the performance of the semantic classification approach remains approximately the same.