Artwork 3D model database indexing and classification

“…Second row: a saliency map representing a hierarchical segmentation of the surface. A framework for the indexing and retrieval of ancient artwork 3D models, using shape descriptors adapted to the surface regions of the segmentations, is detailed in [42]. The mesh is provided by the French Museum Center for Research and Restoration (C2RMF, Le Louvre, Paris).…”

Section: Combinations Of Hierarchiesmentioning

confidence: 99%

Hierarchical Segmentations with Graphs: Quasi-flat Zones, Minimum Spanning Trees, and Saliency Maps

et al. 2017

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Hierarchies of partitions are generally represented by dendrograms (direct representation). They can also be represented by saliency maps or minimum spanning trees. In this article, we precisely study the links between these three representations. In particular, we provide a new bijection between saliency maps and hierarchies based on quasi-flat zones as often used in image processing and we characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasiflat zones preservation. In practice, these results make up a toolkit for designing new hierarchical methods where one can choose the most convenient representation. They also invite us to process non-image data with morphological hierarchies. More precisely, we show the practical interest of the proposed framework for: i) hierarchical watershed image segmentations, ii) combinations of different hierarchical segmentations, iii) hierarchicalizations of some non-hierarchical image segmentation methods based on regional dissimilarities, and iv) hierarchical analysis of geographical data.

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“…The color of the surface of the 3D VRML model was used as input vectors to the Hopfield neural network. Philipp-Foliguet et al [20] proposed a framework for indexing and retrieving 3D archaeological art. The framework includes two parts that support the search and classification of global and local models.…”

Section: B 3d Model Classificationmentioning

confidence: 99%

Deep Belief Network Based 3D Models Classification in Building Information Modeling

Wang¹,

Zhen²,

Xu³

2015

Int. J. Onl. Eng.

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Abstract-3D model classification is a critical process of building information modeling (BIM). A deep learning approach was proposed to classify 3D models in BIM environments. A ray-based feature extraction algorithm was used to extract the features of 3D models and form a features matrix. The deep belief network (DBN) constructed by restricted Boltzmann machines applies a features matrix and classifies the models, adopting the effective training process. The process of training DBN is layer by layer. Experiments were taken from a public 3D model library of a PSB model database. The results show that compared with several commonly used classification methods, the method proposed in this paper achieved good results in the 3D model classification for efficient BIM.

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Artwork 3D model database indexing and classification

Cited by 24 publications

References 41 publications

Collapses and Watersheds in Pseudomanifolds of Arbitrary Dimension

Collapses and Watersheds in Pseudomanifolds of Arbitrary Dimension

Hierarchical Segmentations with Graphs: Quasi-flat Zones, Minimum Spanning Trees, and Saliency Maps

Deep Belief Network Based 3D Models Classification in Building Information Modeling

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