An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid framework for detecting the semantics of concepts and context
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Learning in region-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image classification for content-based indexing
IEEE Transactions on Image Processing
A physical model-based approach to detecting sky in photographic images
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Attention-driven image interpretation with application to image retrieval
Pattern Recognition
Segmentation and description of natural outdoor scenes
Image and Vision Computing
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
An interactive approach for filtering out junk images from keyword-based google search results
IEEE Transactions on Circuits and Systems for Video Technology
Investigating visual feature extraction methods for image annotation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
A novel graph kernel based SVM algorithm for image semantic retrieval
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Image annotation using high order statistics in non-Euclidean spaces
Journal of Visual Communication and Image Representation
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Multi-level annotation of images is a promising solution to enable semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to interpret and annotate the semantics of natural images by using both the dominant image components and the relevant semantic image concepts. In contrast to the well-known image-based and region-based approaches, we use the concept-sensitive salient objects as the dominant image components to achieve automatic image annotation at the content level. By using the concept-sensitive salient objects for image content representation and feature extraction, a novel image classification technique is developed to achieve automatic image annotation at the concept level. To detect the concept-sensitive salient objects automatically, a set of detection functions are learned from the labeled image regions by using support vector machine (SVM) classifiers with an automatic scheme for searching the optimal model parameters. To generate the semantic image concepts, the finite mixture models are used to approximate the class distributions of the relevant concept-sensitive salient objects. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. In addition, a large number of unlabeled samples have been integrated with a limited number of labeled samples to achieve more effective classifier training and knowledge discovery. We have also demonstrated that our algorithms are very effective to enable multi-level interpretation and annotation of natural images.