Efficient Clustering and Matching for Object Class Recognition

In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering gives significant improvement in terms of efficiency while maintaining the same quality of clusters. We also propose a method for building data structures for fast matching in high dimensional feature spaces. These improvements allow to deal with large sets of training data typically used in recognition of multiple object classes.

[1]  G. N. Lance,et al.  A general theory of classificatory sorting strategies: II. Clustering systems , 1967, Comput. J..

[2]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[5]  Michel Bruynooghe,et al.  Méthodes nouvelles en classification automatique de données taxinomiques nombreuses , 1977 .

[6]  H. Edelsbrunner,et al.  Efficient algorithms for agglomerative hierarchical clustering methods , 1984 .

[7]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[8]  V. Ramasubramanian,et al.  A generalized optimization of the K-d tree for fast nearest-neighbour search , 1989, Fourth IEEE Region 10 International Conference TENCON.

[9]  Jeffrey K. Uhlmann,et al.  Satisfying General Proximity/Similarity Queries with Metric Trees , 1991, Inf. Process. Lett..

[10]  J.-P. Benzécri,et al.  Rappel : Construction d'une classification ascendante hiérarchique par la recherche en chaîne des voisins réciproques , 1997 .

[11]  Kris Popat,et al.  Cluster-based probability model and its application to image and texture processing , 1997, IEEE Trans. Image Process..

[12]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Sameer A. Nene,et al.  A simple algorithm for nearest neighbor search in high dimensions , 1997 .

[14]  Piotr Indyk,et al.  Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

[15]  Pietro Perona,et al.  Unsupervised Learning of Models for Recognition , 2000, ECCV.

[16]  Andrew W. Moore,et al.  The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data , 2000, UAI.

[17]  George Karypis,et al.  Evaluation of hierarchical clustering algorithms for document datasets , 2002, CIKM '02.

[18]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[20]  Charles Elkan,et al.  Using the Triangle Inequality to Accelerate k-Means , 2003, ICML.

[21]  N. Goodwin,et al.  Learning to Detect Objects in Images via a Sparse, Part-Based Representation , 2004 .

[22]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[23]  Andrew W. Moore,et al.  An Investigation of Practical Approximate Nearest Neighbor Algorithms , 2004, NIPS.

[24]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Alexei A. Efros,et al.  Discovering object categories in image collections , 2005 .

[28]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Bernt Schiele,et al.  Multiple Object Class Detection with a Generative Model , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).