A new manifold distance measure for visual object categorization
Fengfu Li; Xiayuan Huang; Hong Qiao; Bo Zhang
Conference NamearXiv
Source PublicationarXiv
Conference Datenone
Conference Placenone
AbstractManifold distances are very effective tools for visual object recognition. However, most of the traditionalmanifold 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 traditionalmanifold 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 objectcategorization. Experiments on Coil-20, Coil-100 and Olivetti Face Databases show that the proposeddistance measure is better for visual object categorization than both the traditional manifold distances and the CW-SSIM index based distances.
Document Type会议论文
Corresponding AuthorFengfu Li
Recommended Citation
GB/T 7714
Fengfu Li,Xiayuan Huang,Hong Qiao,et al. A new manifold distance measure for visual object categorization[C],2016.
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