A New Manifold Distance Measure for Visual Object Categorization
This work addresses visual object recognition for computer vision applications, but it is incremental as it builds on existing manifold distance and CW-SSIM methods.
The paper tackled the problem of visual object categorization by proposing a new manifold distance based on the Complex Wavelet Structural Similarity index, which is more robust to image rotations and translations, and experiments on Coil-20, Coil-100, and Olivetti Face Databases showed it outperforms traditional and CW-SSIM-based distances.
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.