71,945 research outputs found

    Knowledge Discovery using Various Multimedia Data Mining Technique

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    Knowledge discovery in databases (KDD) is the process of discovering positive information from a gathering of data. This generally used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior information on data sets and interpreting perfect solutions from the observed results. Knowledge Discovery in Databases is the process of finding knowledge in huge amount of data where data mining is the core of this process. Data mining can be used to understandable meaningful patterns from huge databases and these patterns may be transformed into knowledge. Multimedia data mining can be defined as the process of finding motivating patterns from media data such as audio mining , video mining, image mining and text mining that are not generally available by basic queries and associated results. It is the mining of knowledge and high level multimedia information from large multimedia database system. Multimedia data mining refers to sample discovery, rule extraction and knowledge gaining from multimedia database. In this paper, An Overview On various Multimedia Data technique is given and the main focus is given to the video Data Mining. DOI: 10.17762/ijritcc2321-8169.15035

    Data mining technology for the evaluation of learning content interaction

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    Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

    Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

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    Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order toComment: IEEE Multimedia Big Data (BigMM 2015

    Decision level ensemble method for classifying multi-media data

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    In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly

    Robust Face Recognition for Data Mining

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    While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

    A Survey on Web Usage Mining, Applications and Tools

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    World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content.  As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities

    Mining multimedia salient concepts for incremental information extraction

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    We propose a novel algorithm for extracting information by mining the feature space clusters and then assigning salient concepts to them. Bayesian techniques for extracting concepts from multimedia usually suffer either from lack of data or from too complex concepts to be represented by a single statistical model. An incremental information extraction approach, working at different levels of abstraction, would be able to handle concepts of varying complexities. We present the results of our research on the initial part of an incremental approach, the extraction of the most salient concepts from multimedia information

    Spatio-Temporal Multimedia Big Data Analytics Using Deep Neural Networks

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    With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era, where new opportunities and challenges appear with the high diversity multimedia data together with the huge amount of social data. Nowadays, multimedia data consisting of audio, text, image, and video has grown tremendously. With such an increase in the amount of multimedia data, the main question raised is how one can analyze this high volume and variety of data in an efficient and effective way. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, there is insufficient research that provides a comprehensive framework for multimedia big data analytics and management. To address the major challenges in this area, a new framework is proposed based on deep neural networks for multimedia semantic concept detection with a focus on spatio-temporal information analysis and rare event detection. The proposed framework is able to discover the pattern and knowledge of multimedia data using both static deep data representation and temporal semantics. Specifically, it is designed to handle data with skewed distributions. The proposed framework includes the following components: (1) a synthetic data generation component based on simulation and adversarial networks for data augmentation and deep learning training, (2) an automatic sampling model to overcome the imbalanced data issue in multimedia data, (3) a deep representation learning model leveraging novel deep learning techniques to generate the most discriminative static features from multimedia data, (4) an automatic hyper-parameter learning component for faster training and convergence of the learning models, (5) a spatio-temporal deep learning model to analyze dynamic features from multimedia data, and finally (6) a multimodal deep learning fusion model to integrate different data modalities. The whole framework has been evaluated using various large-scale multimedia datasets that include the newly collected disaster-events video dataset and other public datasets
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