Adaptation for multiple cue integration

Authors:
Zhaohui Sun
Affiliations:
Electric Imaging Products, Research & Development, Eastman Kodak Company, Rochester, NY
Venue:
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Year:
2003

Citing 6
Cited 4

Similarity metric learning for a variable-kernel classifier

Neural Computation
Error characterization of the factorization method

Computer Vision and Image Understanding
Self-Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Contour and Texture Analysis for Image Segmentation

International Journal of Computer Vision
Data Fusion for Sensory Information Processing Systems

Data Fusion for Sensory Information Processing Systems
Segmentation Using Eigenvectors: A Unifying View

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2

Discriminative cue integration for medical image annotation

Pattern Recognition Letters
Image Comparison by Compound Disjoint Information with Applications to Perceptual Visual Quality Assessment, Image Registration and Tracking

International Journal of Computer Vision
Cue integration through discriminative accumulation

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Feature relationships hypergraph for multimodal recognition

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many classification tasks can be carried out by casting a domain-specific problem to a general graph representation (with objects to be organized as graph nodes and pairwise similarities as graph edges) followed by graph partition. In this paper, an adaptation scheme is proposed to integrate multiple graphs from various cues to a single graph, such that the distance between the ideal transition probability matrix to the one derived from cue integration is minimized. Four different distance measures, i.e., the Frobenius norm, the Kullback-Leibler directed divergence, the Jeffrey divergence and the cross entropy, are investigated to minimize the discrepancy. It is shown that the minimization leads to a closed-form nonlinear optimization that can be solved by the Levenberg-Marguardt method. Domain- and task-specific knowledge is explored to facilitate the generic pattern classification task. Experimental results are demonstrated for image content description by multiple cue integration.