Faculty Publications
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Item An information theoretic similarity measure for unified multimedia document retrieval(Institute of Electrical and Electronics Engineers Inc., 2014) Pushpalatha, K.; Ananthanarayana, V.S.Due to the rapid evolution in multimedia technology, the multimedia data have been growing at a phenomenal rate. With the enormous amount of multimedia data, the richness of its information has raised the demand for sophisticated multimedia knowledge discovery systems. Multimedia documents requires distinct type of processing and knowledge discovery methods, due to the distinct characteristics of multimedia data. However, the individual processing and analyzing each type of multimedia data may make the system more tedious and complex. Also, the correlations between the contents of multimedia document are not considered in the knowledge extraction process. When the multimodal objects are represented in the unimodal domain, similar processing and knowledge discovery methods can be used. In this paper, a domain converting method is proposed with the aim to represent the multimedia document of multimodal objects as multimedia document of signal objects. In order to find the similarity between the multimedia documents, an information theory based similarity measure is proposed. Evaluation of the proposed framework has been performed by experimenting the system for the retrieval of multimedia documents. The proposed domain converting method presents the multimodal document in the unified representation. Experimental results of multimedia document retrieval shows better document retrieval rate with the proposed similarity measure. © 2014 IEEE.Item An unified approach for multimedia document representation and document similarity(Institute of Electrical and Electronics Engineers Inc., 2015) Pushpalatha, K.; Ananthanarayana, V.S.In the recent years, the evolution in multimedia technology has accelerated the growth of multimedia data. Even though the multimedia data are heterogeneous, the rich information they carry has made a high demand for sophisticated multimedia knowledge discovery systems. To mine the knowledge from multimedia document, each type of multimedia data has to undergo unique processing and knowledge discovery processes because of its uniqueness. However, this procedure of processing and analyzing each type of multimedia data separately may make the system more complicated in case of large databases. Alternatively, it will be more advantageous if heterogeneous objects are represented in a common domain, such that the similar processing and knowledge discovery methods can be used. Motivated by this concept, a method known as domain converter is proposed to represent the heterogeneous multimedia objects in a spatial domain. Also based on information theory, a similarity measure is proposed to find the similarity between the documents. To evaluate the proposed framework, the experiments have been conducted for the retrieval of multimedia documents. The proposed domain converter represents the multimedia document in a homogeneous domain, and with the proposed similarity measure, better document retrieval rate has been achieved. © 2014 IEEE.Item A New Glowworm Swarm Optimization Based Clustering Algorithm for Multimedia Documents(Institute of Electrical and Electronics Engineers Inc., 2016) Pushpalatha, K.; Ananthanarayana, V.S.Due to the explosion of multimedia data, the demand for the sophisticated multimedia knowledge discovery systems has been increased. The multimodal nature of multimedia data is the big barrier for knowledge extraction. The representation of multimodal data in a unimodal space will be more advantageous for any mining task. We initially represent the multimodal multimedia documents in a unimodal space by converting the multimedia objects into signal objects. The dynamic nature of the glowworms motivated us to propose the Glowworm Swarm Optimization based Multimedia Document Clustering (GSOMDC) algorithm to group the multimedia documents into topics. The better purity and entropy values indicates that the GSOMDC algorithm successfully clusters the multimedia documents into topics. The goodness of the clustering is evaluated by performing the cluster based retrieval of multimedia documents with better precision values. © 2015 IEEE.Item Multimedia Document Mining using Sequential Multimedia Feature Patterns(Institute of Electrical and Electronics Engineers Inc., 2020) Pushpalatha, K.; Ananthanarayana, V.S.Recent years have witnessed the expeditious progress in multimedia technology and rapid growth of multimedia documents. The enormous amount of multimedia documents require sophisticated multimedia mining methods to analyze and utilize the multimodal information. The multimodal objects of a multimedia document are described by the patterns of features. The feature pattern sequences are used to identify the contextual information of the multimedia documents. In this paper, we propose an approach for the discovery of sequential feature patterns from the multimedia documents. The sequential multimedia feature pattern mining generates the multimedia class sequential rules that are used to classify the multimedia documents. The efficiency of the proposed sequential multimedia feature pattern mining is evaluated by experimenting with four datasets of multimedia documents. Experimental results demonstrate that the proposed sequential feature pattern mining can be efficiently used for the knowledge mining from multimedia documents. © 2020 IEEE.Item Feature pattern based representation of multimedia documents for efficient knowledge discovery(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Pushpalatha, K.; Ananthanarayana, V.S.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.Item A tree based representation for effective pattern discovery from multimedia documents(Elsevier B.V., 2017) Pushpalatha, K.; Ananthanarayana, A.The growing amount of multimedia documents demanded the efficient knowledge discovery systems. The efficacy of the knowledge discovery systems is influenced by the representation of multimedia documents. The suitable multimedia document representation acts as a platform for multimedia mining tasks. In this paper, a Multimedia Suffix Tree Document model (MSTD) is presented to represent the multimedia documents in a tree based structure. The MSTD model discovers the useful patterns embedded in the multimedia documents and reduces the search time thereby aiding the multimedia mining methods. It provides the complete information of the multimedia documents in one structure. In order to evaluate the proficiency of the proposed MSTD model, the MSTD model based mining methods are proposed. The experiments are conducted with three multimodal multimedia document datasets. The experimental analysis of the proposed methods reveal the significance of MSTD representation for multimedia documents in achieving the significant performance of multimedia mining tasks. © 2016 Elsevier B.V.