A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel region-based image retrieval method using relevance feedback
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
Automatic Sign Detection and Recognition in Natural Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
ClickRemoval: interactive pinpoint image object removal
Proceedings of the 13th annual ACM international conference on Multimedia
Proceedings of the 13th annual ACM international conference on Multimedia
ICML '05 Proceedings of the 22nd international conference on Machine learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Tagging over time: real-world image annotation by lightweight meta-learning
Proceedings of the 15th international conference on Multimedia
Automatic medical image annotation and retrieval
Neurocomputing
Semi-supervised learning of object categories from paired local features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Semi-automatically labeling objects in images
IEEE Transactions on Image Processing
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Labeling objects in images is an essential prerequisite for many visual learning and recognition applications that depend on training data, such as image retrieval, object detection and recognition. Manually creating labels in images is not only time-consuming but also subject to human labeling errors, and eventually, becomes impossible for a large scale image database. Semi-supervised learning (SSL)algorithms such as Gaussian random field (GRF)can be applied to labeling objects in images since they have the ability to include a large amount of unlabeled data while requiring only a small amount of labeled data. However, the one-shot property of GRF prevents it from achieving good labeling performance. In this paper, we presents a novel object labeling tool, SmartLabel, to semi-automatically label objects in images. The algorithm of SmartLabel has four innovations over GRF:1)soft labeling,2)graph construction with spatial constraints, 3)iterated harmonic energy minimization, and 4)using relevance feedback to incorporate human interaction in the loop. As demonstrated in datasets of six object categories, the proposed SmartLabel not only works effectively even with a very small amount of user input (e.g., 1 .5%of image size)but also achieves significant improvement over GRF.