By Abdelhamid Bouchachia (eds.)
This publication constitutes the court cases 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 lawsuits including the abstracts of four invited talks, have been conscientiously reviewed and chosen from 32 submissions. The contributions are prepared less than the next topical sections: advances in function choice; clustering and type; adaptive optimization; advances in time sequence analysis.
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Extra resources for Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014, Bournemouth, UK, September 8-10, 2014. Proceedings
Thus, SVMs are very attractive for scenes classiﬁcation. SVM classiﬁcation is essentially a binary (two-class) classiﬁcation technique, which has to be modiﬁed to handle the multiclass tasks based upon one-vs-all trained SVMs. Therefore, we have used a multi-class classiﬁer based upon one-vs-all trained SVMs. 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.
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.
The performance of a scene categorization system depends mainly on two components, namely, image representation and classiﬁcation. Image representation is the task of choosing the best features or signatures which can describe and summarize important visual properties of the image. There are mainly two types of features, local and global. Global descriptors consist of features computed on the whole image, therefore, they have the ability to represent the entire image with a single vector. Consequently, they don’t need any further representation and can be directly used for classiﬁcation.
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