By Abdelhamid Bouchachia (eds.)
This booklet constitutes the court cases of the overseas convention on Adaptive and clever platforms, ICAIS 2014, held in Bournemouth, united kingdom, in September 2014. the nineteen complete papers incorporated in those lawsuits including the abstracts of four invited talks, have been conscientiously reviewed and chosen from 32 submissions. The contributions are geared up less than the subsequent topical sections: advances in characteristic choice; clustering and category; adaptive optimization; advances in time sequence analysis.
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Additional info for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings
We finally conclude in Section 4 and present some ideas to be studied in our future work on the topic. 2 Application to Texture Classification A common way to obtain proportional data from images is to work with a BoW strategy. In our work, the quality of the extracted features is not crucial to assess the performance of a classifier relatively to other classifiers, and we therefore simply use SIFT  and a two-stage k-means clustering to build a texton-dictionary. HMMDM has in practice a big restriction on input data dimension that we empirically found to be of the order of 10 (higher dimensions lead intermediate matrices to be singular, causing invertibility issues).
1. The experimental results presented here have been obtained fixing M = K = 3 and P = 12 with random training sets of N = 5, 10, 20 images of each class, running the algorithm 50 times. Results are reported in Figs 2 and 3. 7% on the UIUC one (only accuracy is reported on the graphs). As mentioned earlier, the choice of working with a 10-word dictionary is induced by the HMMDM model itself. Using more words degrades the overall performance mainly because of the divergence of the estimation algorithm for some classes.
In: Proc. ICCV (1999) 14. : Texture classification using noncausal hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 1010–1014 (1995) 15. : Unsupervised texture classification: Automatically discover and classify texture patterns. Image and Vision Computing 26(5), 647–656 (2008) 16. : An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986) 17. : Texture classification by means of hmm modeling of am-fm features. In: Proc. ICIP, pp.
Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings by Abdelhamid Bouchachia (eds.)