By Abdelhamid Bouchachia (eds.)
This booklet constitutes the lawsuits of the foreign convention on Adaptive and clever platforms, ICAIS 2014, held in Bournemouth, united kingdom, in September 2014. the nineteen complete papers integrated in those complaints including the abstracts of four invited talks, have been conscientiously reviewed and chosen from 32 submissions. The contributions are geared up below the next topical sections: advances in function choice; clustering and type; adaptive optimization; advances in time sequence analysis.
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Additional resources for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings
From experimental results, we can conclude that the AGGM outperform the two other methods for scene categorization. Furthermore, we found that using images from all classes at once for training has higher eﬃciency because it takes account of discriminative information between diﬀerent classes. Table 1. 86% 38 T. Elguebaly and N. Bouguila (a) (b) (c) (d) (e) (f) (g) (h) Fig. 1. Sample images from the SUN dataset; First Row: (a) Abbey, (b) Airplane cabin, (c) Airport terminal, (d) Alley, (e) Amusement arcade, (f) Amusement park, (g) Apartment building, (h) Apse; Second Row: (a) Aquarium, (b) Cabin, (c) Candy store, (d) Oﬃce, (e) Oilrig, (f) Parking Garage, (g) Raft, (h) Village 4 Conclusion In this study, an improved visual vocabulary approach is implemented for extracting important keypoints in scenes.
4 Baseline Method To quantify the performance of the HMMDM classifier, a baseline method using an NN classifier is implemented. The Bhattacharyya distance between two histograms G and H d(G, H) = 1− G(i) i H(i) , (1) 24 E. Epaillard, N. Bouguila, and D. Ziou where i denotes the bin number , can be straightforwardly generalized to series of N histograms by d(GN , HN ) = 1 1− N N Gn (i) Hn (i) . n=1 (2) i The number of histograms in our case is equal to the number of patches in the image. However, this distance, denoted BD1 later, is clearly not robust to translation.
In , local regions are also detected and then normalized to be mapped into subspaces. Texton-dictionaries of size 100 are built from different feature types (intensity and gradient) with several linear and non-linear embedding methods and a Support Vector Machine (SVM) classifier is used. In , a method based on the projection of a set of points from a high-dimensional space to a randomly chosen low-dimensional subspace, referred to as random projections (RP), is developed. Images are seen as an ensemble of patches from which RP features are extracted.