MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local image representations using pruned salient points with applications to CBIR
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Using multiple segmentations for image auto-annotation
Proceedings of the 6th ACM international conference on Image and video retrieval
Visual language modeling for image classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Combining stroke-based and selection-based relevance feedback for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Graph-based multiple-instance learning for object-based image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Sparse Multiscale Patches (SMP) for Image Categorization
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
A Convex Method for Locating Regions of Interest with Multi-instance Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
On the sparseness of 1-norm support vector machines
Neural Networks
Evaluating multi-class multiple-instance learning for image categorization
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Scene categorization using boosted back-propagation neural networks
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Learning sparse features on-line for image classification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Semantic image clustering using object relation network
CVM'12 Proceedings of the First international conference on Computational Visual Media
A bag-of-semantics model for image clustering
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.