Automatic text processing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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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
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning in Content-Based Image Retrieval
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Learning image semantics from users relevance feedback
Proceedings of the 12th annual ACM international conference on Multimedia
Content-based sub-image retrieval using relevance feedback
Proceedings of the 2nd ACM international workshop on Multimedia databases
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
OCRS: an interactive object-based image clustering and retrieval system
Multimedia Tools and Applications
Web Image Clustering Based on Multi-instance
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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Image and Vision Computing
A Bayesian approach integrating regional and global features for image semantic learning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Statistical modeling and conceptualization of natural images
Pattern Recognition
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Object-based image retrieval beyond visual appearances
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback for the Earth Mover's distance
AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
Use of image regions in context-adaptive image classification
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Region-Based image retrieval with perceptual colors
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Toward a higher-level visual representation for content-based image retrieval
Multimedia Tools and Applications
Wildlife video key-frame extraction based on novelty detection in semantic context
Multimedia Tools and Applications
Automatic semantic annotation by using fuzzy theory for natural images
International Journal of Wireless and Mobile Computing
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In this paper, several effective learning algorithms using global image representations are adjusted and introduced to regionbased image retrieval (RBIR). First, the query point movement technique is considered. By assembling all the segmented regions of positive examples together and resizing the regions to emphasize the latest positive examples, a composite image is formed as the new query. Second, the application of support vector machines (SVM) in relevance feedback for RBIR is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Last, a region re-weighting algorithm is proposed inspired by those feature re-weighting ones. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed learning algorithms.