  Manifold distances are very effective tools for visual object recognition.
However, most of the traditional manifold distances between images are based on
the pixel-level comparison and thus easily affected by image rotations and
translations. In this paper, we propose a new manifold distance to model the
dissimilarities between visual objects based on the Complex Wavelet Structural
Similarity (CW-SSIM) index. The proposed distance is more robust to rotations
and translations of images than the traditional manifold distance and the
CW-SSIM index based distance. In addition, the proposed distance is combined
with the $k$-medoids clustering method to derive a new clustering method for
visual object categorization. Experiments on Coil-20, Coil-100 and Olivetti
Face Databases show that the proposed distance measure is better for visual
object categorization than both the traditional manifold distances and the
CW-SSIM index based distances.
