Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based Multimedia Retrieval System

In the process of content-based multimedia retrieval, multimedia information is processed in order to obtain descriptive features. Descriptive representation of features, results in a huge feature count, which results in processing overhead. To reduce this descriptive feature overhead, various approaches have been used to dimensional reduction, among which PCA and LDA are the most used methods. However, these methods do not reflect the significance of feature content in terms of inter-relation among all dataset features. To achieve a dimension reduction based on histogram transformation, features with low significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in comparison to the conventional system.