Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Graphics (TOG)
Exploiting Hierarchy in Text Categorization
Information Retrieval
A few logs suffice to build (almost) all trees (II)
A few logs suffice to build (almost) all trees (II)
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
SMI '04 Proceedings of the Shape Modeling International 2004
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose-Oblivious Shape Signature
IEEE Transactions on Visualization and Computer Graphics
Consistent mesh partitioning and skeletonisation using the shape diameter function
The Visual Computer: International Journal of Computer Graphics
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
International Journal of Computer Vision
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ACM SIGGRAPH Asia 2010 papers
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ACM Transactions on Graphics (TOG)
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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ACM SIGGRAPH 2011 papers
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
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ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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We present a method for organizing a heterogeneous collection of 3D shapes for overview and exploration. Instead of relying on quantitative distances, which may become unreliable between dissimilar shapes, we introduce a qualitative analysis which utilizes multiple distance measures but only in cases where the measures can be reliably compared. Our analysis is based on the notion of quartets, each defined by two pairs of shapes, where the shapes in each pair are close to each other, but far apart from the shapes in the other pair. Combining the information from many quartets computed across a shape collection using several distance measures, we create a hierarchical structure we call categorization tree of the shape collection. This tree satisfies the topological (qualitative) constraints imposed by the quartets creating an effective organization of the shapes. We present categorization trees computed on various collections of shapes and compare them to ground truth data from human categorization. We further introduce the concept of degree of separation chart for every shape in the collection and show the effectiveness of using it for interactive shapes exploration.