The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yima: real-time multimedia storage and 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
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Weighting in k-Means Clustering
Machine Learning
Semantic Organization of Scenes Using Discriminant Structural Templates
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Robust Real-Time Face Detection
International Journal of Computer Vision
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Iteratively clustering web images based on link and attribute reinforcements
Proceedings of the 13th annual ACM international conference on Multimedia
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
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
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It is well accepted that using high-dimensional multi-modal visual features for image content representation and classifier training may achieve more sufficient characterization of the diverse visual properties of the images and further result in higher discrimination power of the classifiers. However, training the classifiers in a high-dimensional multi-modal feature space requires a large number of labeled training images, which will further result in the problem of curse of dimensionality. To tackle this problem, a hierarchical feature subset selection algorithm is proposed to enable more accurate image classification, where the processes for feature selection and classifier training are seamlessly integrated in a single framework. First, a feature hierarchy (i.e., concept tree for automatic feature space partition and organization) is used to automatically partition high-dimensional heterogeneous multi-modal visual features into multiple low-dimensional homogeneous single-modal feature subsets according to their certain physical meanings and each of them is used to characterize one certain type of the diverse visual properties of the images. Second, principal component analysis (PCA) is performed on each homogeneous singlemodal feature subset to select the most representative feature dimensions and a weak classifier is learned simultaneously. After the weak classifiers and their representative feature dimensions are available for all these homogeneous single-modal feature subsets, they are combined to generate an ensemble image classifier and achieve hierarchical feature subset selection. Our experiments on a specific domain of natural images have also obtained very positive results.