Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
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
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
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
A graph-based image annotation framework
Pattern Recognition Letters
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Image annotation via graph learning
Pattern Recognition
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
International Journal of Computer Vision
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
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
This paper presents a generalized relevance model for automatic image annotation through learning the correlations between images and annotation keywords. Unlike previous relevance models, the proposed model can perform keyword propagation not only from the training images to the test ones but also among the test images. We further give a convergence analysis of the iterative algorithm inspired by the proposed model. Moreover, our spatial Markov kernel is used to define the inter-image relations for the estimation of the joint probability of observing an image with possible annotation keywords. This kernel was originally designed for image classification, and here we apply it to image annotation. The main advantage of using our spatial Markov kernel is that we can capture the intra-image context based on 2D Markov models, which is different from the traditional bag-of-words methods. Experiments on two standard image databases demonstrate that the proposed model outperforms the state-of-the-art annotation models.