Elements of information theory
Elements of information theory
Making large-scale support vector machine learning practical
Advances in kernel methods
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
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Vector Quantization and Density Estimation
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
An user preference information based kernel for SVM active learning in content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Divergence Estimation of Continuous Distributions Based on Data-Dependent Partitions
IEEE Transactions on Information Theory
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
| Hi-index | 0.00 |
In this paper, a quantization-based probabilistic feature modeling approach is proposed for relevance feedback in content-based image retrieval. We demonstrate its performance by using the resulting models within a support vector machine (SVM) based technique. Each feature component is quantized and mapped to probabilistic quantities representing the likelihood of the image being relevant (and irrelevant). These probabilistic quantities are then used to derive an information divergence-based kernel function for SVM classification which we introduced in earlier work. We show that the proposed method leads to the optimal maximum likelihood solution as the knowledge of the actual underlying probability model improves (i.e.,as the feature space is partitioned into arbitrarily small "regions "and accurate models are known for all regions). vWe investigate several practical quantization designs for feature modeling specifically in relevance feedback applications,where the scarcity of the data and high dimensionality prevent usage of vector quantization and parametric modeling approaches.Our proposed framework naturally takes into account the statistics of the data that is available during relevance feedback for the purpose of discriminating between relevant and irrelevant images.Experiments with the Corel dataset show that quantizers specifically designed for this application achieve gains over simple uniform quantizers (e.g.,5% to 10% in retrieval accuracy) when combined with our information divergence kernel. This kernel achieves gains (e.g.,17% in retrieval accuracy after first relevance feedback)as compared to the standard radial basis function (RBF) kernel used for SVM-based relevance feedback.