Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Hierarchical Procrustes Matching for Shape Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Co-transduction for shape retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Balancing deformability and discriminability for shape matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Exploiting contextual information for image re-ranking
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Exploiting contextual spaces for image re-ranking and rank aggregation
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Image re-ranking and rank aggregation based on similarity of ranked lists
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Exploiting clustering approaches for image re-ranking
Journal of Visual Languages and Computing
Shape matching and classification using height functions
Pattern Recognition Letters
Exploiting pairwise recommendation and clustering strategies for image re-ranking
Information Sciences: an International Journal
Perceptually motivated morphological strategies for shape retrieval
Pattern Recognition
On the dynamic time warping of cyclic sequences for shape retrieval
Image and Vision Computing
Densifying Distance Spaces for Shape and Image Retrieval
Journal of Mathematical Imaging and Vision
Perceptually motivated shape context which uses shape interiors
Pattern Recognition
Image re-ranking and rank aggregation based on similarity of ranked lists
Pattern Recognition
Multi-feature structure fusion of contours for unsupervised shape classification
Pattern Recognition Letters
Shape retrieval and recognition based on fuzzy histogram
Journal of Visual Communication and Image Representation
A scalable re-ranking method for content-based image retrieval
Information Sciences: an International Journal
Image and Vision Computing
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree
Journal of Mathematical Imaging and Vision
Using contextual spaces for image re-ranking and rank aggregation
Multimedia Tools and Applications
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This paper considers two major applications of shape matching algorithms: (a) query-by-example, i e retrieving the most similar shapes from a database and (b) finding clusters of shapes, each represented by a single prototype Our approach goes beyond pairwise shape similarity analysis by considering the underlying structure of the shape manifold, which is estimated from the shape similarity scores between all the shapes within a database We propose a modified mutual kNN graph as the underlying representation and demonstrate its performance for the task of shape retrieval We further describe an efficient, unsupervised clustering method which uses the modified mutual kNN graph for initialization Experimental evaluation proves the applicability of our method, e g by achieving the highest ever reported retrieval score of 93.40% on the well known MPEG-7 database.