Modalities consensus for multi-modal constraint propagation

Authors:
Zhenyong Fu;Hongtao Lu;Horace H.S. Ip;Zhiwu Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong;Institute of Computer Science and Technology, Peking University, Beijing, China
Venue:
Proceedings of the 20th ACM international conference on Multimedia
Year:
2012

Citing 5
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral clustering and transductive learning with multiple views

Proceedings of the 24th international conference on Machine learning
Spectral learning

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Constrained spectral clustering via exhaustive and efficient constraint propagation

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Multi-modal constraint propagation for heterogeneous image clustering

MM '11 Proceedings of the 19th ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel modalities consensus framework for multi-modal pairwise constraint propagation (MCP). We first combine multiple single-modal constraint propagation (SCP) problems together, and then explicitly introduce a new modalities consensus regularizer to force the propagation results on different modalities to be consistent with each other. With a separable consensus regularizer, the proposed approach can be effectively solved using an alternating optimization way. More importantly, based on our modalities consensus framework, two single-modal constraint propagation algorithms can be directly reformulated as two well-defined multi-modal solutions. Experimental results on constrained clustering tasks have shown that the proposed framework can achieve significant improvements with respect to the state of the arts.