A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth 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
Learning from ambiguity
MPEG-7 video automatic labeling system
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust Real-Time Face Detection
International Journal of Computer Vision
Cross-Modality Automatic Face Model Training from Large Video Databases
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Exploring temporal consistency for video analysis and retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Enhanced Sports Image Annotation and Retrieval Based Upon Semantic Analysis of Multimodal Cues
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Deriving semantic terms for images by mining the web
Proceedings of the 11th International Conference on Electronic Commerce
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In this paper, we propose an autonomous learning scheme to automatically build visual semantic concept models from the output data of Internet search engines without any manual labeling work. First of all, images are gathered by crawling through the Internet using a search engine such as Google. Then, we model the search results as "Quasi-Positive Bags" in the Multiple-Instance Learning (MIL) framework. We call this generalized MIL (GMIL). We propose an algorithm called "Bag K-Means" to find the maximum Diverse Density (DD) without the existence of negative bags. A cost function is found as K-Means with special "Bag Distance". We also propose a solution called "Uncertain Labeling Density" (ULD) which describes the target density distribution of instances in the case of quasi-positive bags. A "Bag Fuzzy K-Means" is presented to get the maximum of ULD. By this generalized MIL with ULD, the model for a particular concept is learned from the crawled images of the Internet search engines. Experiments show that our algorithm can get correct models for the concepts we are interested in. Compared to the original Google Image Search, our algorithm shows improved accuracy.