In recent years, new harmonic analysis tools providing sparse representation in high dimension space have been proposed. In particular, ridgelets and curvelets bases are similar to the sparse components of naturally occurring image data derived empirically by computational neuroscience researchers. Ridgelets take the form of basis elements which exhibit very high directional sensitivity and are highly anisotropic. The ridgelet transform have been shown to provide a sparse representation for smooth objects with straight edges. Independently, for the purpose of scene description, the shape of the Fourier energy spectra has been used as an efficient way to provide a “holistic” description of the scene picture and its semantic category. Similarly, we focus on a simple binary semantic classification (artificial vs. natural) based on various ridgelet features. The learning stage is performed on a large image database using different state of the art Linear Discriminant techniques. Classification results are compared with those resulting from the Gabor representation. Additionally, ridgelet representation provides us with a way to accurately reconstruct the original signal. Using this synthesis step, we filter the ridgelet coefficients with the discriminant vector. The resulting image identifies the elements within the scene contributing to the different perceptual dimensions.