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
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
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
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Latent semantic fusion model for image retrieval and annotation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Improve Image Annotation by Combining Multiple Models
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A novel latent variable modeling technique for image annotation and retrieval is proposed. This model is useful for annotating the images with relevant semantic meanings as well as for retrieving images which satisfy the users query with specific text or image. The framework of two-step latent variable is proposed to support multi-functionality of the retrieval and annotation system. Furthermore, the existing and the proposed image annotation models are compared in terms of their annotating performance. Images from standard databases are used in the comparison in order to identify the best model for automatic image annotation, using precision-recall measurement. Local features, or visual words, of each image in the database are extracted using Scale-Invariant Feature Transform (SIFT) and clustering techniques. Each image is then represented by Bag-of-Features (BoF) which is a histogram of visual words. Semantic meanings can then be related to each BoF using latent variable for annotation purposes. Subsequently, for image retrieval, each image query is also related to semantic meanings. Finally, image retrieval results are obtained by matching semantic meanings of the query with those of the images in the database using a second latent variable.