Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Localized content based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective and efficient object-based image retrieval using visual phrases
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Typicality ranking via semi-supervised multiple-instance learning
Proceedings of the 15th international conference on Multimedia
Video annotation by graph-based learning with neighborhood similarity
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
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Graph-based multiple-instance learning for object-based image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Instance-level semisupervised multiple instance learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
Localized content-based image retrieval using semi-supervised multiple instance learning
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-Layer Multi-Instance Learning for Video Concept Detection
IEEE Transactions on Multimedia
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations
IEEE Transactions on Multimedia
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Object-based image retrieval has been an active research topic in recent years, in which a user is only interested in some object in the images. As one promising approach, graph-based multi-instance learning has attracted many researchers. The existing methods often conduct learning on one graph, either in image level or in region level. While in this paper, by considering both image- and region-level information at the same time, a novel method based on multi-graph multi-instance learning is proposed. Two graphs are constructed in our method, and the relationship between each image and its segmented regions is introduced into an optimization framework. Moreover, our method is further extended to video retrieval. By exploring the relationships between video shots, representative images, and segmented regions, it can deal with the case when training labels are only assigned in shot level. Experimental results on the SIVAL image benchmark and the TRECVID video set demonstrate the effectiveness of our proposal.