We proposed [1] a feature binding method to generate a MPEG-7 compliant feature vector, defined as C-MP7. Here, we study the excellence of C-MP7 as a feature vector, using either low- or high-dimensional chaos. With high-dimensional chaos-based C-MP7, we find, 1) the accuracy in SVM classifier improves 10% to 20%, for all classes of video objects over MPEG-7, 2) larger binary class separation among video objects in different classes, 3) vehicle objects are clustered well, which leads to above 99% accuracy for only vehicles against other objects in SVM, and 4) drifts in chaotic attractors allow the C-MP7 to include subtle variations in coefficients for video objects. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Azhar, H., & Amer, A. (2010). High dimensional versus low dimensional chaos in MPEG-7 feature binding for object classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6134 LNCS, pp. 315–323). https://doi.org/10.1007/978-3-642-13681-8_37
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