COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Representation and learning in information retrieval
Representation and learning in information retrieval
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Color matching for image retrieval
Pattern Recognition Letters
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bootstrapping statistical parsers from small datasets
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Hierarchical browsing and search of large image databases
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
Interactive high-dimensional index for large Chinese calligraphic character databases
ACM Transactions on Asian Language Information Processing (TALIP)
Device parts retrieval from assembly drawings with SVM based active relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Aggregate similarity queries in relevance feedback methods for content-based image retrieval
Proceedings of the 2008 ACM symposium on Applied computing
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
On multi-view active learning and the combination with semi-supervised learning
Proceedings of the 25th international conference on Machine learning
Semi-supervised document retrieval
Information Processing and Management: an International Journal
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
New Labeling Strategy for Semi-supervised Document Categorization
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Learning with unlabeled data and its application to image retrieval
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
SVM-based active feedback in image retrieval using clustering and unlabeled data
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Relevance feature mapping for content-based image retrieval
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Mass estimation and its applications
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive retrieval of targets for wide area surveillance
Proceedings of the international conference on Multimedia
Local-feature-based image retrieval with weighted relevance feedback
International Journal of Business Intelligence and Data Mining
Neighborhood preserving regression for image retrieval
Neurocomputing
Software defect detection with rocus
Journal of Computer Science and Technology
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
A relevance feedback-based learner for image retrieval using SIFT descriptors
International Journal of Computational Vision and Robotics
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
Computer Vision and Image Understanding
An extension of the aspect PLSA model to active and semi-supervised learning for text classification
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Multimodal indexing based on semantic cohesion for image retrieval
Information Retrieval
Unlabeled data and multiple views
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
An adaptive image retrieval system with relevance feedback and clustering
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Machine Learning
Bidirectional-isomorphic manifold learning at image semantic understanding & representation
Multimedia Tools and Applications
Co-metric: a metric learning algorithm for data with multiple views
Frontiers of Computer Science: Selected Publications from Chinese Universities
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Effective balancing error and user effort in interactive handwriting recognition
Pattern Recognition Letters
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Relevance feedback is an effective scheme bridging the gap between high-level semantics and low-level features in content-based image retrieval (CBIR). In contrast to previous methods which rely on labeled images provided by the user, this article attempts to enhance the performance of relevance feedback by exploiting unlabeled images existing in the database. Concretely, this article integrates the merits of semisupervised learning and active learning into the relevance feedback process. In detail, in each round of relevance feedback two simple learners are trained from the labeled data, that is, images from user query and user feedback. Each learner then labels some unlabeled images in the database for the other learner. After retraining with the additional labeled data, the learners reclassify the images in the database and then their classifications are merged. Images judged to be positive with high confidence are returned as the retrieval result, while those judged with low confidence are put into the pool which is used in the next round of relevance feedback. Experiments show that using semisupervised learning and active learning simultaneously in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.