A language modeling approach to information retrieval
A language modeling approach to information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Title language model for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
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
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Evaluating the impact of selection noise in community-based web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Automatic image annotation and retrieval using weighted feature selection
Multimedia Tools and Applications
Knowledge Based Image Annotation Refinement
Journal of Signal Processing Systems
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Multi-modal multi-label semantic indexing of images based on hybrid ensemble learning
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Improving image annotations using wordnet
MIS'05 Proceedings of the 11th international conference on Advances in Multimedia Information Systems
Semi-supervised learning for image annotation based on conditional random fields
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
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The goal of automatic image annotation is to automatically generate annotations for images to describe their content. In the past, statistical machine translation models have been successfully applied to automatic image annotation task [8]. It views the process of annotating images as a process of translating the content from a 'visual language' to textual words. One problem with the existing translation models is that common words are usually associated with too many different image regions. As a result, uncommon words have little chance to be used for annotating images. Uncommon words are important for automatic image annotation because they are often used in the queries. In this paper, we propose two modified translation models for automatic image annotation, namely the normalized translation model and the regularized translation model, that specifically address the problem of common annotated words. The basic idea is to raise the number of blobs that are associated with uncommon words. The normalized translation model realizes this by scaling translation probabilities of different words with different factors. The same goal is achieved in the regularized translation model through the introduction of a special Dirichlet prior. Empirical study with the Corel dataset has shown that both two modified translation models outperform the original translation model and several existing approaches for automatic image annotation substantially.