Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Mode Estimation of Probabilistic Hybrid Systems
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
International Journal of Bio-Inspired Computation
International Journal of Wireless and Mobile Computing
International Journal of Bio-Inspired Computation
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
International Journal of Wireless and Mobile Computing
Hi-index | 0.00 |
In this paper, the multi-instance learning algorithm is improved under the image retrieval framework based on contents, and the improved multi-instance learning algorithm is applied to image retrieval to better handle the ambiguity of the image. In this method, the image is used as the multi-instance bag and is divided into multiple instances by image segmentation algorithm, and then the multi-instance learning is performed with the multi-objective-diverse-density algorithm. The learning results are ordered by image similarity using the vector space model. Finally, relevant feedback is given in accordance with the positive bag and negative bag chosen by the user to provide satisfactory results to the user.