Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
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Multi-layer multi-instance kernel for video concept detection
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SVM-based active feedback in image retrieval using clustering and unlabeled data
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Automatic medical image annotation and retrieval
Neurocomputing
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
State-of-the-art on spatio-temporal information-based video retrieval
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A genetic programming framework for content-based image retrieval
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A new feature selection method for Gaussian mixture clustering
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Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Image retrieval using nonlinear manifold embedding
Neurocomputing
An efficient and effective image representation for region-based image retrieval
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An efficient region-based image representation using Legendre color distribution moments
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A New Incremental PCA Algorithm With Application to Visual Learning and Recognition
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Biased ISOMap projections for interactive reranking
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Biased discriminant euclidean embedding for content-based image retrieval
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SVM-based active feedback in image retrieval using clustering and unlabeled data
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MAP classifier with BDA features
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Online learning of relevance feedback from expert readers for mammogram retrieval
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Local-feature-based image retrieval with weighted relevance feedback
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Speed up kernel discriminant analysis
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Hessian optimal design for image retrieval
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Connected component in feature space to capture high level semantics in CBIR
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Active multiple kernel learning for interactive 3D object retrieval systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Sparse transfer learning for interactive video search reranking
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Synthetic pattern generation for imbalanced learning in image retrieval
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Dual-force metric learning for robust distracter-resistant tracker
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KIMEL: A kernel incremental metalearning algorithm
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Multiple kernel local Fisher discriminant analysis for face recognition
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Ordinal regularized manifold feature extraction for image ranking
Signal Processing
3D CBIR with sparse coding for image-guided neurosurgery
Signal Processing
Content-based retrieval of human actions from realistic video databases
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Facial expression recognition based on Hessian regularized support vector machine
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Generalized mean for feature extraction in one-class classification problems
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MIAPS: A web-based system for remotely accessing and presenting medical images
Computer Methods and Programs in Biomedicine
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In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms