Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Modern Information Retrieval
ACM Transactions on Graphics (TOG)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Content-based Three-dimensional Engineering Shape Search
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SMI '04 Proceedings of the Shape Modeling International 2004
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
A pivot-based index structure for combination of feature vectors
Proceedings of the 2005 ACM symposium on Applied computing
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Dynamic similarity search in multi-metric spaces
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
A powerful relevance feedback mechanism for content-based 3D model retrieval
Multimedia Tools and Applications
Content-Based 3D Object Retrieval
IEEE Computer Graphics and Applications
Structural Shape Prototypes for the Automatic Classification of 3D Objects
IEEE Computer Graphics and Applications
Localized Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Weighting visual features with pseudo relevance feedback for CBIR
Proceedings of the ACM International Conference on Image and Video Retrieval
3D object retrieval using an efficient and compact hybrid shape descriptor
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Isometry-invariant matching of point set surfaces
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Similarity score fusion by ranking risk minimization for 3D object retrieval
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Learning the compositional structure of man-made objects for 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'10 track: feature detection and description
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
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Effective content-based retrieval in 3D model databases is an important problem that has attracted much research attention over the last years. Many individual methods proposed to date rely on calculating global 3D model descriptors based on image, surface, volumetric, or structural model properties. Descriptors such as these are then input for determining the degree of similarity between models. Traditionally, the ability of individual descriptors to perform effective 3D search is decided by benchmarking. However, in practice the data set on which 3D retrieval is to be applied may differ from the characteristics of the respective benchmark. Therefore, statically determining the descriptor to use based on a fixed benchmark may lead to suboptimal results. We propose a generic strategy to improve the retrieval effectiveness in 3D retrieval systems consisting of multiple model descriptors. The specific contribution of this paper is two-fold. First, we propose to adaptively combine multiple descriptors by forming weighted descriptor combinations, where the weight of each descriptor is decided at query time. Second, we enhance the set of global model descriptors to be combined by including partial descriptors of the same kind in the combinations. Partial descriptors are obtained by applying a given descriptor extractor on the set of parts of a model, obtained by a simple model partitioning scheme. Thereby, more model information is exposed to the 3D descriptors, leading to a more complete object description. We give a systematic discussion of the descriptor combination space involving static and query-adaptive weighting schemes, and based on descriptors of different type and focus (model global vs. partial). The combination of both global and partial model descriptors is shown to deliver improved retrieval precision, compared to policies using single descriptors or fixed-weight combinations. The resulting scheme is generic and can accommodate a large class of global 3D model descriptors.