Automatic structuring and retrieval of large text files
Communications of the ACM
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
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
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Graph-based multiple-instance learning for object-based image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Fuzzy aesthetic semantics description and extraction for art image retrieval
Computers & Mathematics with Applications
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
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
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Focusing on the problem of localized content-based image retrieval, based on probabilistic latent semantic analysis (PLSA) and transductive support vector machine (TSVM), a novel semi-supervised multi-instance learning (SSMIL) algorithm is proposed, where a bag corresponds to an image and an instance corresponds to the low-level visual features of a segmented region. In order to convert an MIL problem into a standard supervised learning problem, first, all the instances in training bags be clustered by K-Means method, and regards each cluster center as ''visual-word'' to build a visual vocabulary. Second, according to the distance between ''visual-word'' and instance, a fuzzy membership function is defined to establish a fuzzy term-document matrix, then use PLSA method to obtain bag's (image's) latent topic models, which can convert every bag to a single sample. Finally, in order to use the unlabeled images to improve retrieval accuracy, using semi-supervised TSVM to train classifiers. Experimental results on the COREL data sets show that the proposed method, named PLSA-SSMIL, is robust, and its performance is superior to other key existing MIL algorithms.