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Artificial Intelligence
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Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Incorporating shape into spatially-aware adaptive object segmentation algorithm
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
Constructing target concept in multiple instance learning using maximum partial entropy
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Learning-based object segmentation using regional spatial templates and visual features
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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We present AKDDE, an adaptive kernel diverse density estimate scheme for multiple instance learning. AKDDE revises the definition of diverse density as the kernel density estimate of diverse positive bags. We show that the AKDDE is inversely proportional to the least bound that contains at least one instance from each positive bag. In order to incorporate the influence of negative bags an objective function is constructed as the difference between the AKDDE of positive bags and the kernel density estimate of negative ones. This scheme is simple in concept and has better properties than other MIL methods. We validate AKDDE on both synthetic and real-world benchmark MIL datasets.