Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Learning semantic categories for 3D model retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Retrieving articulated 3-D models using medial surfaces
Machine Vision and Applications
Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features
Proceedings of the ACM International Conference on Image and Video Retrieval
Distance metric learning and feature combination for shape-based 3D model retrieval
Proceedings of the ACM workshop on 3D object retrieval
SHREC'12 track: generic 3D shape retrieval
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
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This paper proposes an improvement to Manifold Ranking algorithm used for search results ranking in the context of shape-based 3D model retrieval. Manifold Ranking algorithm by Zhou et al estimates, given a set of high-dimensional feature vectors, a lower-dimensional manifold on which the features lie. It then computes diffusion-based distances from a feature vector (or feature vectors) to the other feature vectors on the manifold. When applied to content-based retrieval, overall retrieval accuracy is significantly better than a "simple" fixed distance metric. However, in a small neighborhood of query, retrieval ranks obtained by a "simple" distance metric (e.g., L1-norm) performs better than those obtained by Manifold Ranking. Proposed re-ranking algorithm tries to combine ranking results due to both simple distance metric and Manifold Ranking in an automatic query expansion framework for better ranking results. Experimental evaluation has shown that the proposed method is effective in improving retrieval accuracy.