COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from ambiguity
Active learning: theory and applications
Active learning: theory and applications
Convex Optimization
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Localized content based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Active learning with statistical models
Journal of Artificial Intelligence Research
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiple-view multiple-learner active learning
Pattern Recognition
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
A multiple component matching framework for person re-identification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Remote sensing image segmentation by active queries
Pattern Recognition
Inconsistency-based active learning for support vector machines
Pattern Recognition
Multi-graph multi-instance learning for object-based image and video retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper, we propose two general multiple-instance active learning (MIAL) methods, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple-instance active learning with fisher information (F-MIAL), and apply them to the active learning in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one, while F-MIAL utilizes the fisher information and analyzes the value of the unlabeled pictures by assigning different labels to them. In experiments, we will show their superior performances in LCBIR tasks.