Automatic medical image annotation and retrieval
Neurocomputing
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
A genetic programming framework for content-based image retrieval
Pattern Recognition
Stochastic modeling western paintings for effective classification
Pattern Recognition
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Texture image retrieval based on non-tensor product wavelet filter banks
Signal Processing
A local Tchebichef moments-based robust image watermarking
Signal Processing
Computational Statistics & Data Analysis
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
IEEE Transactions on Image Processing
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Co-training with relevant random subspaces
Neurocomputing
Geometric distortion insensitive image watermarking in affine covariant regions
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Summarizing tourist destinations by mining user-generated travelogues and photos
Computer Vision and Image Understanding
Active multiple kernel learning for interactive 3D object retrieval systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Image retrieval for alzheimer's disease detection
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Oracle in Image Search: A Content-Based Approach to Performance Prediction
ACM Transactions on Information Systems (TOIS)
Support vector machine for breast MR image classification
Computers & Mathematics with Applications
When Amazon Meets Google: Product Visualization by Exploring Multiple Web Sources
ACM Transactions on Internet Technology (TOIT)
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively