The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A model of multimedia information retrieval
Journal of the ACM (JACM)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Semantic feedback for interactive image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Automated binary texture feature sets for image retrieval
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Joint semantics and feature based image retrieval using relevance feedback
IEEE Transactions on Multimedia
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
3C intelligent home appliance control system - Example with refrigerator
Expert Systems with Applications: An International Journal
Image and Vision Computing
Antinoise texture retrieval based on PCNN and one-class SVM
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
| Hi-index | 12.05 |
In order to narrow semantic gap between user query concept and low-level features in content-based image retrieval, SVM-based relevance feedback techniques are developed to learn user's query concept by labeling some samples. The major difficulty in relevance feedback is to estimate the support of target image in high-dimensional feature space with small number of training samples. To overcome this limitation, we propose an ensemble method to boost image retrieval accuracy and to improve its generalization performance. Images are segmented into multiple instances. A set of moderate accurate one-class support vector machine classifiers are trained separately by using different sub-features extracted from instances. The ensemble method results in a highly accurate by combining moderately accurate weak classifiers. Our propose ensemble scheme not only provides a robust mechanism in selecting strong query concept related images for relevant feedback, but also achieves a generalization performance in image retrieval.