Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hidden Markov models for automatic annotation and content-based retrieval of images and video
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning techniques for business blog search and mining
Expert Systems with Applications: An International Journal
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An HMM-SVM-based automatic image annotation approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
Margin-maximizing classification of sequential data with infinitely-long temporal dependencies
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Automatic image annotation (AIA) is an effective technology to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on hidden Markov model (HMM). Compared with the previous HMM-based annotation methods, SVM based semi-supervised learning, i.e. transductive SVM (TSVM), is triggered out for remarkably boosting the reliability of HMM with less users' labeling effort involved (denoted by TSVM-HMM). This guarantees that the proposed TSVM-HMM based annotation scheme integrates the discriminative classification with the generative model to mutually complete their advantages. In addition, not only the relevance model between the visual content of images and the textual keywords but also the property of keyword correlation is exploited in the proposed AIA scheme. Particularly, to establish an enhanced correlation network among keywords, both co-occurrence based and WordNet based correlation techniques are well fused and are able to be helpful for benefiting from each other. The final experimental results reveal that the better annotation performance can be achieved at less labeled training images.