4 April 2001 Achieving semantic coupling in the domain of high-dimensional video indexing application
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In this paper, an adequately domain-independent approach is presented where local features can characterize multimedia data using Neural Networks (ANN) and Support Vector Machines (SVM). In our previous work, we have shown that classification in content-based retrieval requires non- linear mapping of feature space. This can normally be accomplished by ANN and SVM. However, they inherently lack the capability to deal with meaningful feature evaluation and large dimensional feature space in the sense that they are inaccurate and slow. These defects can be overcome by employing meaningful feature selection on the basis of discriminative capacity of a feature. The experiments on database consisting of real video sequences show that the speed and accuracy of SVM can be improved substantially using this technique, while execution time can be substantially reduced for ANN. The comparison also shows that improved SVM turns out to be a better choice than ANN. Finally, it is shown that generalization in learning is not affected by reducing the dimension of the feature space by our method.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ankush Mittal, Ankush Mittal, Loong-Fah Cheong, Loong-Fah Cheong, } "Achieving semantic coupling in the domain of high-dimensional video indexing application", Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001); doi: 10.1117/12.420931; https://doi.org/10.1117/12.420931


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