Support vector machine active learning for image retrieval

Authors:
Simon Tong;Edward Chang
Affiliations:
Stanford University, Sanford, CA;University of California, Santa Barbara, CA
Venue:
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Year:
2001

Citing 25
Cited 293

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AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Image searching and browsing by active aspect-based relevance learning

CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Non-metric similarity ranking for image retrieval

DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Applying active learning to assertion classification of concepts in clinical text

Journal of Biomedical Informatics
Asymmetric bayesian learning for image retrieval with relevance feedback

MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Biased minimax probability machine active learning for relevance feedback in content-based image retrieval

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Unlabeled data and multiple views

PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
A conversation with Dr. Edward Y. Chang

ACM SIGKDD Explorations Newsletter
Active surveying: a probabilistic approach for identifying key opinion leaders

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Multi-modal region selection approach for training object detectors

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Classification based group photo retrieval with bag of people features

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Active learning for social image retrieval using Locally Regressive Optimal Design

Neurocomputing
A Learning to Rank framework applied to text-image retrieval

Multimedia Tools and Applications
Parallel field ranking

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active query sensing: Suggesting the best query view for mobile visual search

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section of best papers of ACM multimedia 2011, and special section on 3D mobile multimedia
Enhancing image retrieval by an exploration-exploitation approach

MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Content-based image retrieval using color difference histogram

Pattern Recognition
Efficient targeted search using a focus and context video browser

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Personalized music emotion classification via active learning

Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Attribute feedback

Proceedings of the 20th ACM international conference on Multimedia
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Attribute feedback

Proceedings of the 20th ACM international conference on Multimedia
Failure prediction based on log files using Random Indexing and Support Vector Machines

Journal of Systems and Software
Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting

Engineering Applications of Artificial Intelligence
Learning a ground object manifold for interpreting high-resolution sensor image

AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Mammogram Retrieval: Image Selection Strategy of Relevance Feedback for Locating Similar Lesions

International Journal of Digital Library Systems
Performance Evaluation of Relevance Feedback for Image Retrieval by "Real-World" Multi-Tagged Image Datasets

International Journal of Multimedia Data Engineering & Management
Asymmetric propagation based batch mode active learning for image retrieval

Signal Processing
Content-based retrieval of human actions from realistic video databases

Information Sciences: an International Journal
Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval

Proceedings of the 21st ACM international conference on Multimedia
Parallel Field Ranking

ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Content-based diversifying leaf image retrieval

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
An improved distance-based relevance feedback strategy for image retrieval

Image and Vision Computing
Active learning for human action retrieval using query pool selection

Neurocomputing
A loan default discrimination model using cost-sensitive support vector machine improved by PSO

Information Technology and Management
iKernel: Exact indexing for support vector machines

Information Sciences: an International Journal
Semi-supervised learning for character recognition in historical archive documents

Pattern Recognition
Interactive patent classification based on multi-classifier fusion and active learning

Neurocomputing
An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification

Neurocomputing
Pattern classification and clustering: A review of partially supervised learning approaches

Pattern Recognition Letters

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Abstract

Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.