Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Discriminant analysis and eigenspace partition tree for face and object recognition from views
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Multi-class relevance feedback content-based image retrieval
Computer Vision and Image Understanding
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Learning large margin classifiers locally and globally
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
Knowledge and Information Systems
Automatic medical image annotation and retrieval
Neurocomputing
Image categorization: Graph edit distance+edge direction histogram
Pattern Recognition
KPCA for semantic object extraction in images
Pattern Recognition
CyberIR --- A Technological Approach to Fight Cybercrime
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
A genetic programming framework for content-based image retrieval
Pattern Recognition
Image annotation via graph learning
Pattern Recognition
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
A local Tchebichef moments-based robust image watermarking
Signal Processing
Image retrieval using nonlinear manifold embedding
Neurocomputing
Computational Statistics & Data Analysis
Multi-video synopsis for video representation
Signal Processing
IEEE Transactions on Image Processing
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Geometric distortion insensitive image watermarking in affine covariant regions
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Online learning of relevance feedback from expert readers for mammogram retrieval
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Hessian optimal design for image retrieval
Pattern Recognition
Active multiple kernel learning for interactive 3D object retrieval systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Relevance feedback for real-world human action retrieval
Pattern Recognition Letters
Efficient image matching using weighted voting
Pattern Recognition Letters
A two-stage decision model for information filtering
Decision Support Systems
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Content-based retrieval of human actions from realistic video databases
Information Sciences: an International Journal
Fisher kernel based relevance feedback for multimodal video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
MIAPS: A web-based system for remotely accessing and presenting medical images
Computer Methods and Programs in Biomedicine
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Recently, relevance feedback (RF) in content-based image retrieval (CBIR) has been implemented as an online binary classifier to separate the positive samples from the negative samples, where both sets of samples are labeled by the user. In many applications, it is reasonable to assume that all the positive samples are alike and thus that the region of the feature space occupied by the positive samples can be described by a single hypersurface. However, for the negative samples, previous RF methods either treat each one of the negative samples as an isolated point or assume the whole negative set can be described by a single convex hypersurface. In this paper, we argue that these treatments of the negative samples are not sound. Our belief is all positive samples are included in a set and the negative samples split into a small number of subsets, each one of which has a simple distribution. Therefore, we first cluster the negative samples into several groups; for each such negative group, we build a marginal convex machine (MCM) subclassifier between it and the single positive group which results in a series of subclassifiers. These subclassifiers are then incorporated into a biased MCM (BMCM) for RF. Experiments were carried out to prove the advantages of BMCM-based RF over previous methods for RF.