10,254 research outputs found

    Analysis and Forecasting of Trending Topics in Online Media Streams

    Full text link
    Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

    Semi-automatic semantic enrichment of raw sensor data

    Get PDF
    One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

    Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model

    Full text link
    Omnidirectional video enables spherical stimuli with the 360×180360 \times 180^ \circ viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201

    Knowledge Discovery using Various Multimedia Data Mining Technique

    Get PDF
    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
    corecore