Feature pattern based representation of multimedia documents for efficient knowledge discovery

Abstract

The rapid growth of multimedia documents has raised huge demand for sophisticated multimedia knowledge discovery systems. The knowledge extraction of the documents mainly relies on the data representation model and the document representation model. As the multimedia document comprised of multimodal multimedia objects, the data representation depends on modality of the objects. The multimodal objects require distinct processing and feature extraction methods resulting in different features with different dimensionalities. Managing multiple types of features is challenging for knowledge extraction tasks. The unified representation of multimedia document benefits the knowledge extraction process, as they are represented by same type of features. The appropriate document representation will benefit the overall decision making process by reducing the search time and memory requirements. In this paper, we propose a domain converting method known as Multimedia to Signal converter (MSC) to represent the multimodal multimedia document in an unified representation by converting multimodal objects as signal objects. A tree based approach known as Multimedia Feature Pattern (MFP) tree is proposed for the compact representation of multimedia documents in terms of features of multimedia objects. The effectiveness of the proposed framework is evaluated by performing the experiments on four multimodal datasets. Experimental results show that the unified representation of multimedia documents helped in improving the classification accuracy for the documents. The MFP tree based representation of multimedia documents not only reduces the search time and memory requirements, also outperforms the competitive approaches for search and retrieval of multimedia documents. 2016, Springer Science+Business Media New York.