International Journal of Computer Vision
Fundamentals of speech recognition
Fundamentals of speech recognition
The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Digital Libraries: Meeting Place for Low-Level And High-Level Vision
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Library-Based Coding: A Representation for Efficient Video Compression and Retrieval
DCC '97 Proceedings of the Conference on Data Compression
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scalable discriminant feature selection for image retrieval and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
The Story Picturing Engine---a system for automatic text illustration
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
An adaptive graph model for automatic image annotation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image annotation by large-scale content-based image retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A Hybrid Model of Image Retrieval Based on Ontology Technology and Probabilistic Ranking
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Faceted search and retrieval based on semantically annotated product family ontology
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
A game based approach to assign geographical relevance to web images
Proceedings of the 18th international conference on World wide web
Robust image annotation refinement via graph-based learning
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Knowledge Based Image Annotation Refinement
Journal of Signal Processing Systems
Information Processing and Management: an International Journal
Baselines for Image Annotation
International Journal of Computer Vision
PATSI: photo annotation through finding similar images with multivariate Gaussian models
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Image annotation with concept level feature using PLSA+CCA
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
A Multi-Directional Search technique for image annotation propagation
Journal of Visual Communication and Image Representation
Some experiments of face annotation based on latent semantic indexing in FIARS
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
EagleRank: a novel ranking model for web image search engine
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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We introduce a new model for semantic annotation and retrieval from image databases. The new model is based on a probabilistic formulation that poses annotation and retrieval as classification problems, and produces solutions that are optimal in the minimum probability of error sense. It is also database centric, by establishing a one-to-one mapping between semantic classes and the groups of database images that share the associated semantic labels. In this work we show that, under the database centric probabilistic model, optimal annotation and retrieval can be implemented with algorithms that are conceptually simple, computationally efficient, and do not require prior semantic segmentation of training images. Due to its simplicity, the annotation and retrieval architecture is also amenable to sophisticated parameter tuning, a property that is exploited to investigate the role of feature selection in the design of optimal annotation and retrieval systems. Finally, we demonstrate the benefits of simply establishing a one-to-one mapping between keywords and the states of the semantic classification problem over the more complex, and currently popular, joint modeling of keyword and visual feature distributions. The database centric probabilistic retrieval model is compared to existing semantic labeling and retrieval methods, and shown to achieve higher accuracy than the previously best published results, at a fraction of their computational cost.