System identification: theory for the user
System identification: theory for the user
The nature of statistical learning theory
The nature of statistical learning theory
Introduction to data compression
Introduction to data compression
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Computing conditional probabilities in large domains by maximizing renyi's quadratic entropy
Computing conditional probabilities in large domains by maximizing renyi's quadratic entropy
SMI '04 Proceedings of the Shape Modeling International 2004
Feature Combination and Relevance Feedback for 3D Model Retrieval
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Incorporating real-valued multiple instance learning into relevance feedback for image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A Minimum Sphere Covering Approach to Pattern Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Bayes classification based on minimum bounding spheres
Neurocomputing
Prototype-based threshold rules
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Content-based object movie retrieval by use of relevance feedback
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Efficient 3-D model search and retrieval using generalized 3-D radon transforms
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
VICTORY: a 3D search engine over P2P and wireless P2P networks
Proceedings of the 4th Annual International Conference on Wireless Internet
Multilevel relevance feedback for 3D shape retrieval
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
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Relevance Feedback is a technique used for enhancing retrieval accuracy in multimedia database systems. In this paper two novel relevance feedback algorithms are proposed for 3D object databases, in which the relative scores of various users, which express users' subjectivity, are kept accumulatively as additional descriptors. Each object is interpreted as a charged particle, whose relative scores represent the value of the charge. Based on these charges, semantic forces are calculated between the 3D objects, which are repelled or attracted properly in the feature space. The forces are of dual nature, semantic and geometric, in the first algorithm, whereas they are purely semantic in the second one. Furthermore, a novel algorithm for annotation propagation is developed, which is based on a linear prediction scheme of the changes that must be made in the feature vector of a newly added 3D object in the database, according to alterations that has already taken place to objects in the database. The combination of low and high level features in one formula, is able to fill the semantic gap as much as possible till time being, while the proposed content-free retrieval method illustrates the fact that in the long run, a purely semantic algorithm can provide excellent retrieval results.