Relevance feedback for sketch retrieval based on linear programming classification

Authors:
Bin Li;Zhengxing Sun;Shuang Liang;Yaoye Zhang;Bo Yuan
Affiliations:
State Key Lab for Novel Software Technology, Nanjing University, P.R. China;State Key Lab for Novel Software Technology, Nanjing University, P.R. China;State Key Lab for Novel Software Technology, Nanjing University, P.R. China;State Key Lab for Novel Software Technology, Nanjing University, P.R. China;State Key Lab for Novel Software Technology, Nanjing University, P.R. China
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

Citing 10
Cited 2

Ink as a first-class datatype in multimedia databases

Multimedia database systems
Feature Selection: Evaluation, Application, and Small Sample Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive nearest neighbor search for relevance feedback in large image databases

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
MindReader: Querying Databases Through Multiple Examples

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Relevance Feedback Decision Trees in Content-Based Image Retrieval

CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Sketch Retrieval Based on Spatial Relations

CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Informal user interface for graphical computing

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Learning from examples in the small sample case: face expression recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments

IEEE Transactions on Image Processing

Sketch retrieval and relevance feedback with biased SVM classification

Pattern Recognition Letters
Active BSVM learning for relevance feedback in content-based sketch retrieval

SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Relevance feedback plays as an important role in sketch retrieval as it does in existing content-based retrieval. This paper presents a method of relevance feedback for sketch retrieval by means of Linear Programming (LP) classification. A LP classifier is designed to do online training and feature selection simultaneously. Combined with feature selection, it can select a set of user-sensitive features and perform classification well facing a small number of training samples. Experiments prove the proposed method both effective and efficient for relevance feedback in sketch retrieval.