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
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
Semi-supervised learning with graphs
Semi-supervised learning with graphs
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Multiple-Instance learning via random walk
ECML'06 Proceedings of the 17th European conference on Machine Learning
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MR-MIL: Manifold Ranking Based Multiple-Instance Learning for Automatic Image Annotation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Expert Systems with Applications: An International Journal
Proceedings of the ACM International Conference on Image and Video Retrieval
Ordinal preserving projection: a novel dimensionality reduction method for image ranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Multi-graph multi-instance learning for object-based image and video retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Most of the existing methods for natural scene categorization only consider whether a sample is relevant or irrelevant to a particular concept. However, for the samples relevant to a certain concept, their typicalities or relevancy scores to the concept generally are different. Typicality measure should be taken into account to make the categorization results more consistent with human's perception. In this paper, we propose a novel typicality ranking scheme for categorizing natural scenes through a two-stage semi-supervised multiple-instance learning method. The first stage infers the typicalities of the underlying positive instances (i.e., regions in images) in the training dataset and the second one predicts the typicality of each bag (i.e., image) in a semi-supervised manner. Compared to existing typicality ranking approaches, the main advantages of the proposed method lie in twofold. First, it only needs image-level labels instead of region-level ones in the training stage. Second, it is fully automated and no human feedback is required. Experiments conducted on a COREL image dataset demonstrate the effectiveness of the proposed approach.