Integrated graph-based semi-supervised multiple/single instance learning framework for image annotation

Authors:
Jinhui Tang;Haojie Li;Guo-Jun Qi;Tat-Seng Chua
Affiliations:
National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;University of Science and Technology of China, Hefei, China;National University of Singapore, Singapore, Singapore
Venue:
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Year:
2008

Citing 16
Cited 5

Solving the multiple instance problem with axis-parallel rectangles

Artificial Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Image Categorization by Learning and Reasoning with Regions

The Journal of Machine Learning Research
Graph based multi-modality learning

Proceedings of the 13th annual ACM international conference on Multimedia
MISSL: multiple-instance semi-supervised learning

ICML '06 Proceedings of the 23rd international conference on Machine learning
Label propagation through linear neighborhoods

ICML '06 Proceedings of the 23rd international conference on Machine learning
MILES: Multiple-Instance Learning via Embedded Instance Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation refinement using random walk with restarts

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Typicality ranking via semi-supervised multiple-instance learning

Proceedings of the 15th international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection

Proceedings of the 15th international conference on Multimedia
Optimizing multi-graph learning: towards a unified video annotation scheme

Proceedings of the 15th international conference on Multimedia
Semi-Supervised Learning

Semi-Supervised Learning
Multiple-Instance learning via random walk

ECML'06 Proceedings of the 17th European conference on Machine Learning
Video Annotation Based on Kernel Linear Neighborhood Propagation

IEEE Transactions on Multimedia

Combining similarity measures in content-based image retrieval

Pattern Recognition Letters
Video semantic analysis based on structure-sensitive anisotropic manifold ranking

Signal Processing
Correlative linear neighborhood propagation for video annotation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semi-supervised truth discovery

Proceedings of the 20th international conference on World wide web
Image annotation using high order statistics in non-Euclidean spaces

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, many learning methods based on multiple-instance (local) or single-instance (global) representations of images have been proposed for image annotation. Their performances on image annotation, however, are mixed as for certain concepts the single-instance representations of images are more suitable, while for some other concepts the multiple-instance representations are better. Thus in this paper, we explore an unified learning framework that combines the multiple-instance and single-instance representations for image annotation. More specifically, we propose an integrated graph-based semi-supervised learning framework to utilize these two types of representations simultaneously, and explore an effective and computationally efficient strategy to convert the multiple-instance representation into a single-instance one. Experiments conducted on the Coral image dataset show the effectiveness and efficiency of the proposed integrated framework.