High dimensional versus low dimensional chaos in MPEG-7 feature binding for object classification

Authors:
Hanif Azhar;Aishy Amer
Affiliations:
Electrical and Computer Engineering, Concordia University, Montréal, Québec, Canada;Electrical and Computer Engineering, Concordia University, Montréal, Québec, Canada
Venue:
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Year:
2010

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Abstract

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.