tailieunhanh - AUTOMATION & CONTROL - Theory and Practice Part 15

Tham khảo tài liệu 'automation & control - theory and practice part 15', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Image Retrieval System in Heterogeneous Database 341 linear separation in this space where this time it should be more adapted. The kernel functions can achieve this projection and must check a number of properties to ensure the effectiveness of this technique so you do not have to make calculations in very large dimensions. With the kernel functions we can work in very large dimensions. However a linear separation and a linear regression is facilitated by the projection of data in a space of high dimension. Projecting in the space of descriptors and using an algorithm to maximize the margin SVM managed to get a severability retaining good generalization capacity is the central idea of SVM. For more details on SVMs we refer interested readers to Cristianini Taylor 2000 . A comparison between SVM-multiclass as supervised classification and Euclidian distance based k-means as unsupervised classification is presented in Kachouri et al. 2008b . The obtained results prove that SVM classifier outperforms the use of similarity measures chiefly to classify heterogeneous image database. Therefore we integrate SVM classifier in our proposed image retrieval systems in this chapter. 5. Image recognition and retrieval results through relevant features selection To ensure a good feature selection during image retrieval we present and discuss the effectiveness of the different feature kind and aggregation. Since heterogeneous image database contains various images presenting big content difference. The idea to introduce a system optimization tool was essential when one realized during the carried out tests that the use of all extracted features could be heavy to manage. Indeed more features vectors dimensions are significant more the classifier has difficulties for their classification. The traditional way that one followed in Djouak et al. 2005a and that one finds in many CBIR systems is a diagram which consists of the use of all extracted features in the classification step. .