Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An introduction to boosting and leveraging
Advanced lectures on machine learning
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Evolutionary Feature Synthesis for Image Databases
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Hybrid coevolutionary algorithms vs. SVM algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Feature synthesized EM algorithm for image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Immune multiobjective optimization algorithm for unsupervised feature selection
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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As a commonly used unsupervised learning algorithm in Content-Based Image Retrieval (CBIR), Expectation-Maximization (EM) algorithm has several limitations, especially in high dimensional feature spaces where the data are limited and the computational cost varies exponentially with the number of feature dimensions. Moreover, the convergence is guaranteed only at a local maximum. In this paper, we propose a unified framework of a novel learning approach, namely Coevolutionary Feature Synthesized Expectation-Maximization (CFS-EM), to achieve satisfactory learning in spite of these difficulties. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm. The advantages of CFS-EM are: 1) it synthesizes low-dimensional features based on CGP algorithm, which yields near optimal nonlinear transformation and classification precision comparable to kernel methods such as the support vector machine (SVM); 2) the explicitness of feature transformation is especially suitable for image retrieval because the images can be searched in the synthesized low-dimensional space, while kernel-based methods have to make classification computation in the original high-dimensional space; 3) the unlabeled data can be boosted with the help of the class distribution learning using CGP feature synthesis approach. Experimental results show that CFS-EM outperforms pure EM and CGP alone, and is comparable to SVM in the sense of classification. It is computationally more efficient than SVM in query phase. Moreover, it has a high likelihood that it will jump out of a local maximum to provide near optimal results and a better estimation of parameters.