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
Efficient propagation for face annotation in family albums
Proceedings of the 12th annual ACM international conference on Multimedia
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable search-based image annotation of personal images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
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
Canonical contextual distance for large-scale image annotation and retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
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Web image annotation is a very important method to effectively index and search images on the internet. Many web image annotation approaches utilized not only the visual information but also the texts emerged with web image. However, they failed to utilize the whole relations among annotations, which reflect the specific semantic content of images. In this paper we propose a novel Multi-Progressive Model (MPM) for web image annotation that leverages word correlations between available texts of web images and an automatic-built vocabulary. The proposed approach treat the available text of web images as initial annotations, and extend them by using a pre-defined lexicon to include more words which are potentially relevant to the target image. It then rank initial and extended annotations by taking advantage of whole words relations without bringing huge computation. The multi-progressive model can be viewed as a greedy optimization algorithm that approximately optimizes the joint annotation probability in a progressive way. Experimental results on web images demonstrate the effectiveness of the proposed model.