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
Machine Learning
Pattern Recognition Letters
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Methodology for Mapping Scores to Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Intelligent Indexing and Semantic Retrieval of Multimodal Documents
Information Retrieval
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improving Image Retrieval with Semantic Classification Using Relevance Feedback
Proceedings of the IFIP TC2/WG2.6 Sixth Working Conference on Visual Database Systems: Visual and Multimedia Information Management
Context and configuration-based scene classification
Context and configuration-based scene classification
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A Two-Stage Classifier for Broken and Blurred Digits in Forms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
Semantics and feature discovery via confidence-based ensemble
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
EXTENT: fusing context, content, and semantic ontology for photo annotation
Proceedings of the 2nd international workshop on Computer vision meets databases
Dual diffusion model of spreading activation for content-based image retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
Automatic Image Annotation with Relevance Feedback and Latent Semantic Analysis
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Boosting One-Class Support Vector Machines for Multi-Class Classification
Applied Artificial Intelligence
TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation
Expert Systems with Applications: An International Journal
Semantics-preserving bag-of-words models for efficient image annotation
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Parallel algorithms for mining large-scale rich-media data
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Multi-modal multi-label semantic indexing of images based on hybrid ensemble learning
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Hierarchical long-term learning for automatic image annotation
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Modeling, classifying and annotating weakly annotated images using Bayesian network
Journal of Visual Communication and Image Representation
Integrating clustering and supervised learning for categorical data analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Semantics-preserving bag-of-words models and applications
IEEE Transactions on Image Processing
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
Collection-based sparse label propagation and its application on social group suggestion from photos
ACM Transactions on Intelligent Systems and Technology (TIST)
A review on automatic image annotation techniques
Pattern Recognition
Probability model of covering algorithm (PMCA)
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Semi-supervised learning for image annotation based on conditional random fields
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
VLSI design of an SVM learning core on sequential minimal optimization algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Using Hilbert scan on statistical color space partitioning
Computers and Electrical Engineering
Toward supervised anomaly detection
Journal of Artificial Intelligence Research
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
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We propose using one-class, two-class, and multiclass SVMs to annotate images for supporting keyword retrieval of images. Providing automatic annotation requires an accurate mapping of images' low-level perceptual features (e.g., color and texture) to some high-level semantic labels (e.g., landscape, architecture, and animals). Much work has been performed in this area; however, there is a lack of ability to assess the quality of annotation. In this paper, we propose a confidence-based dynamic ensemble (CDE), which employs a three-level classification scheme. At the base-level, CDE uses one-class Support Vector Machines (SVMs) to characterize a confidence factor for ascertaining the correctness of an annotation (or a class prediction) made by a binary SVM classifier. The confidence factor is then propagated to the multiclass classifiers at subsequent levels. CDE uses the confidence factor to make dynamic adjustments to its member classifiers so as to improve class-prediction accuracy, to accommodate new semantics, and to assist in the discovery of useful low-level features. Our empirical studies on a large real-world data set demonstrate CDE to be very effective.