Automatic structuring and retrieval of large text files
Communications of the ACM
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiple instance learning with generalized support vector machines
Eighteenth national conference on Artificial intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
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
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Texture image retrieval based on non-tensor product wavelet filter banks
Signal Processing
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Image categorization via robust pLSA
Pattern Recognition Letters
Directional Hartley transform and content based image retrieval
Signal Processing
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
Multiple instance learning based on positive instance selection and bag structure construction
Pattern Recognition Letters
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Focusing on the problem of natural image retrieval, based on latent semantic analysis (LSA) and support vector machine (SVM), a novel multi-instance learning (MIL) algorithm is proposed, where a bag corresponds to an image and an instance corresponds to the low-level visual features of a segmented region. Firstly, in order to transform every bag into a single sample, a collection of ''visual-word'' is generated by k-means clustering method to construct a projection space, then a nonlinear mapping is defined using these ''visual-word'' to embed each bag as a point in the projection space, thereby obtaining every bag's projection feature. Secondly, the matrix consisted of all the projection features of training bags is regarded as a term-document matrix, and LSA method is used to obtain the latent semantic feature of each bag. As a result, the MIL problem is converted into a standard single instance learning (SIL) problem that can be solved directly by SVM method. Experimental results on the COREL data sets show that the proposed method, named LSASVM-MIL, is robust, and its performance is superior to other key existing MIL algorithms.