Rapid and brief communication: Active learning for image retrieval with Co-SVM

Authors:
Jian Cheng;Kongqiao Wang
Affiliations:
Beijing University of Posts and Telecommunications, Beijing 100876, China and Nokia Research Center, He Ping Li Dong Jie No. 11, Nokia House 1, Beijing 100013, China;Nokia Research Center, He Ping Li Dong Jie No. 11, Nokia House 1, Beijing 100013, China
Venue:
Pattern Recognition
Year:
2007

Citing 3
Cited 16

Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Selective Sampling with Redundant Views

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence

Dynamic Distance-Based Active Learning with SVM

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Watch, Listen & Learn: Co-training on Captioned Images and Videos

ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Nearest neighbor editing aided by unlabeled data

Information Sciences: an International Journal
Selective sampling based on dynamic certainty propagation for image retrieval

MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Kernel subclass convex hull sample selection method for SVM on face recognition

Neurocomputing
An ACO-based algorithm for parameter optimization of support vector machines

Expert Systems with Applications: An International Journal
Hessian optimal design for image retrieval

Pattern Recognition
SALSAS: Sub-linear active learning strategy with approximate k-NN search

Pattern Recognition
Incremental kernel learning for active image retrieval without global dictionaries

Pattern Recognition
Remote sensing image segmentation by active queries

Pattern Recognition
Inconsistency-based active learning for support vector machines

Pattern Recognition
Hybrid active learning for reducing the annotation effort of operators in classification systems

Pattern Recognition
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
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
Asymmetric propagation based batch mode active learning for image retrieval

Signal Processing
Interactive patent classification based on multi-classifier fusion and active learning

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In relevance feedback algorithms, selective sampling is often used to reduce the cost of labeling and explore the unlabeled data. In this paper, we proposed an active learning algorithm, Co-SVM, to improve the performance of selective sampling in image retrieval. In Co-SVM algorithm, color and texture are naturally considered as sufficient and uncorrelated views of an image. SVM classifiers are learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples which are differently classified by the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval.