Models of incremental concept formation
Artificial Intelligence
Adaptation in natural and artificial systems
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Binary digital image processing: a discrete approach
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ACM Computing Surveys (CSUR)
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Digital watermarking
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computational Optimization and Applications
A Secure, Robust Watermark for Multimedia
Proceedings of the First International Workshop on Information Hiding
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ISCC '00 Proceedings of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000)
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Support Vector Data Description
Machine Learning
Adaptive two-level watermarking for binary document images
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IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
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SMEM Algorithm for Mixture Models
Neural Computation
Machine learning based adaptive watermark decoding in view of anticipated attack
Pattern Recognition
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Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Journal of Visual Communication and Image Representation
Split-merge incremental learning (SMILE) of mixture models
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Intelligent Watermarking of Document Images as a Dynamic Optimization Problem
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
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ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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Applied Soft Computing
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ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Intelligent reversible watermarking in integer wavelet domain for medical images
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IEEE Transactions on Signal Processing
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IEEE Transactions on Multimedia
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Communications Magazine
Reversible watermarking scheme for medical image based on differential evolution
Expert Systems with Applications: An International Journal
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In intelligent watermarking (IW), evolutionary computing (EC) is employed in order to automatically set the embedding parameters of digital watermarking systems for each image. However, the computational complexity of EC techniques makes IW unfeasible for large scale applications involving heterogeneous images. In this paper, we propose a dynamic particle swarm optimization (DPSO) technique which relies on a memory of Gaussian mixture models (GMMs) of solutions in the optimization space. This technique is employed in the optimization of embedding parameters of a multi-level (robust/fragile) bi-tonal watermarking system in high data rate applications. A compact density representation of previously-found DPSO solutions is created through GMM in the optimization space, and stored in memory. Solutions are re-sampled from this memory, re-evaluated for new images and have their distribution of fitness values compared with that stored in the memory. When the distributions are similar, memory solutions are employed in a straightforward manner, avoiding costly re-optimization operations. A specialized memory management mechanism allows to maintain and adapt GMM distributions over time, as the image stream changes. This memory of GMMs allows an accurate representation of the topology of a stream of optimization problems. Consequently, new cases of optimization can be matched against previous cases more precisely (when compared with a memory of static solutions), leading to considerable decrease in computational burden. Simulation results on heterogeneous streams of images indicate that compared to full re-optimization for each document image, the proposed approach allows to decrease the computational requirement linked to EC by up to 97.7% with little impact on the accuracy for detecting watermarks. Comparable results were obtained for homogeneous streams of document images.