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» Learning with Transformation Invariant Kernels
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CVPR
2010
IEEE
13 years 4 months ago
Global and local isometry-invariant descriptor for 3D shape comparison and partial matching
In this paper, based on manifold harmonics, we propose a novel framework for 3D shape similarity comparison and partial matching. First, we propose a novel symmetric meanvalue rep...
Huai-Yu Wu, Hongbin Zha, Tao Luo, Xulei Wang, Song...
CORR
2010
Springer
110views Education» more  CORR 2010»
13 years 4 months ago
Learning Multi-modal Similarity
In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, including nearest-neighbor retrieval, classification,...
Brian McFee, Gert R. G. Lanckriet
COMPGEOM
2011
ACM
12 years 11 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
IVC
2007
176views more  IVC 2007»
13 years 7 months ago
Kernel-based distance metric learning for content-based image retrieval
ct 8 For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system 9 depends critically on the similarity or diss...
Hong Chang, Dit-Yan Yeung
ICIP
2010
IEEE
13 years 5 months ago
A new subspace learning method in Fourier domain for texture classification
This paper proposes a new texture classification approach. There are two main contributions in the proposed method. First, input texture images are transformed to the composite Fo...
Shu Liao, Albert C. S. Chung