Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Streaming with Affinity Propagation
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A local approach of adaptive affinity propagation clustering for large scale data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Speed up image annotation based on LVQ technique with affinity propagation algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
| Hi-index | 0.00 |
In an image semantic annotation system, it often encounters the large-scale and high dimensional feature datasets problem, which leads to a slow learning process and degrading image semantic annotation accuracy. In order to reduce the high time complexity caused by redundancy information of image feature dataset, we adopt an improved affinity propagation (AP) algorithm to improve annotation by extracting and re-grouping the repeated feature points. The time consumption is reduced by square of repetition factor. The experiments results illustrate that the proposed annotation method has excellent time complexity and better annotation precision compared with original AP algorithms.