Improving Affinity Matrices by Modified Mutual kNN-Graphs

The recent progress in describing affinities between images or objects by means of shape, appearance or texture allows the exploitation of inherently emerging redundancies for improvement of retrieval tasks. We propose a two-way normalization and analysis scheme which aims on (a) modeling object interdependence by neighborhood incorporation and (b) retrieval improvement by subsequent analysis from a modified mutual k nearest neighbor graph. We provide a general and flexible approach which may be either applied for improving retrieval quality or as base for semi-supervised classification, clustering or dimensionality reduction methods. The presented experiments demonstrate that our approach yields to significant improvements on a broad variety of data sets, including the highest ever reported bullseye score of 93.40% on the MPEG-7 database.

[1]  Ronald Rosenfeld,et al.  Semi-supervised learning with graphs , 2005 .

[2]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[4]  U. Feige,et al.  Spectral Graph Theory , 2015 .

[5]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[6]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[7]  Eamonn J. Keogh,et al.  Manifold Clustering of Shapes , 2006, Sixth International Conference on Data Mining (ICDM'06).

[8]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Zhuowen Tu,et al.  Improving Shape Retrieval by Learning Graph Transduction , 2008, ECCV.

[11]  Joachim M. Buhmann,et al.  Clustering with the Connectivity Kernel , 2003, NIPS.

[12]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[13]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.