Adaptive and Intelligent Systems: Third International by Abdelhamid Bouchachia (eds.)

By Abdelhamid Bouchachia (eds.)

This booklet constitutes the complaints of the overseas convention on Adaptive and clever structures, ICAIS 2014, held in Bournemouth, united kingdom, in September 2014. the nineteen complete papers incorporated in those court cases including the abstracts of four invited talks, have been conscientiously reviewed and chosen from 32 submissions. The contributions are prepared below the next topical sections: advances in characteristic choice; clustering and class; adaptive optimization; advances in time sequence analysis.

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Extra info for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings

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Dynamical dirichlet mixture model. IDIAP-RR 02, IDIAP (2007) 6. : Estimation of the number of states for gesture recognition with hidden Markov models based on the number of critical points in time sequence. Pattern Recognition Letters 34(5), 574–579 (2013) 7. : The computation of the Bhattacharyya distance between histograms without histograms. In: Proc. IPTA, pp. 373–378 (2010) 28 E. Epaillard, N. Bouguila, and D. Ziou 8. : Wavelet-based texture analysis and synthesis using hidden Markov models.

Algorithm 2. Visual vocabulary construction 1 2 3 Input: Training Set, Mmax ∗ Output: Θsuper ,Msuper Local feature extraction: we use dense PCA-SIFT descriptors of 16 × 16 pixel patches computed over a grid with spacing of 8 pixels. Hence, each patch is modeled by a 36-dimensional vector. if per-category training then 1. Use the AGGM estimation algorithm proposed in 1 to construct a visual dictionary for each scene category. Thus, we will end up with K AGGMs for the K scene categories. 2. Combine the K AGGMs into one super AGGM by simply combining the set of AGGDs together and dividing the mixture components prior probability by the number of categories to ensure they summed to one.

Keywords: Hidden Markov models, Dirichlet mixtures, texture classification, Bhattacharyya distance, bag-of-visual-words. 1 Introduction Most of our natural environment can be interpreted as textures, in the sense it is mainly composed of more or less repetitive patterns involving some spatial dynamics. Their thorough study is of great importance and texture categorization, segmentation, and synthesis have been the topics of unnumbered studies with applications as various as medical imaging [9], special effects [2], remote sensing [21], etc.

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