Metadata Construction Model for Web Videos: a Domain Specific Approach
Total Page：16
File Type：pdf, Size：1020Kb
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 3 Issue 12 December 2014, Page No. 9742-9748 Metadata Construction Model for Web Videos: A Domain Specific Approach Siddu P. Algur1, Prashant Bhat2*, Suraj Jain3 1School of Mathematics and Computing Sciences, Department of Computer Science Rani Channamma University, Belagavi-591156, Karnataka, India [email protected] 2*School of Mathematics and Computing Sciences, Department of Computer Science Rani Channamma University, Belagavi-591156, Karnataka, India [email protected] 3School of Mathematics and Computing Sciences, Department of Computer Science Rani Channamma University, Belagavi-591156, Karnataka, India [email protected] Abstract: The advances in computer and information technology together with the rapid evolution of multimedia data are resulted in the huge growth of the digital video. Due to the rapid growth of digital data and video database over the Internet, it is becoming very important to extract useful information from visual data. The scientific community has increased the amount of research into new technologies, with a view to improve the digital video utilization: its archiving, indexing, accessibility, acquisition, store and even its process and usability. All these parts of the video utilization entail the necessity of the extraction of all important information of a video, especially in cases of lack of metadata information. This paper describes importance of descriptive metadata of video, categorization of video contents based on video descriptive metadata, and high level metadata based web video modeling. The main goal of this paper is to construct high level descriptive metadata with and without timeline for all the category of videos and extraction of metadata from web videos such as YouTube and Face Book. By using the high level descriptive metadata information, a user is facilitated on the one hand to locate a specific video and on the other hand is able to comprehend rapidly the basic points and generally, the main concept of a video without the need to watch the whole of it. Keywords: Descriptive Metadata, Web Video, Metadata Construction Model. tends to be difficult to retrieve from many sites. Further 1. INTRODUCTION compounding the problem, this data is difficult to retrieve in Over the last few years it has been observed an increasingly different ways – creating a page scraper for one site doesn’t use of multimedia and web video data. This fact automatically help retrieve data from others, nor does it help when that site’s entails that not only the transmission but also the store and the design changes [2]. process of the relevant information are crucial. The digital web video could be considered as the most representative example 2. RELATED WORKS of multimedia data. More particularly, every single day a quite In the last decades a lot of significant approaches on the large quantity of digital web videos is produced and therefore, video metadata field are observed. The main objective of these the amount of information seems huge and uncontrollable. works is to facilitate the user to get significant metadata from Thus, video web sites have become overcrowded, since they the video data. In 2013, the authors Dim P. Papadopoulos, have to deal with this high amount of data and with the visiting Vicky S. Kalogeiton, Savvas A. Chatzichristofis, Nikos users. In the year 2011, YouTube video-sharing website had Papamarkos [1] worked on the concept, Automatic more than 1 trillion views, while according to the official summarization and annotation of videos with lack of metadata YouTube statistics, each month, more than 4 billion hours of information. In this method, the authors proposed a new video are watched and over 800 million unique users accessed system that automatically generates and provides all the the site [1]. essential information both in visual and textual form of a Online media have a great number of advantages for social video, in cases of lack of metadata information. This system science researchers: they provide lasting and exacting accounts consists of a key-frame-based video summarization method, of discussions, they contain useful metadata (date, time, responsible for the visual form of information, and of an subject tags, user names, etc.), and they often contain innovative key-word-based video annotation method, comments, discussions, and so on. Unfortunately, this data inextricably linked to the textual information. Siddu P.Algur, IJECS Volume 3 Issue 12 December, 2014 Page No.9742-9748 Page 9742 Video data personalization is increasingly viewed as an unsupervised clustering algorithm to the retrieved metadata essential component of any front-end to a large information linked to the viewers’ assessments of the videos. space. Video data personalization can achieve customization of One of the challenge today is to go for a automated precise the presentation of information. With respect to this aspect, W. alignment of high-level semantic features gained from the Klippgen, T.D.C. Little, G. Ahanger, and D. Venkatesh [3], analysis of complementary sources and the low/mid-level described techniques to facilitate this personalization, features extracted by audio-video analysis, supporting at the overviewed the unique characteristics of the video medium, end a real cross-media search through large archives or through and proposed a framework for personalized news delivery of the web [7]. In this regard, a work has been done by the video information in the news domain. The proposed technique authors T. Declerck, P. Buitelaar, M. Alcantara, M. Labský and is based upon the application and integration of existing V. Svátek [7], is adding semantic metadata to multimedia techniques of video content and structure modeling, metadata material, on the base of the results of the automatic analysis collection, vector space analysis, personalization and filtering, applied to associated language material, being speech metadata management, and video and audio composition. The transcripts or various types of textual documents related to core concept is the use of a set of vector-based and time- video/image material. dependent filters for audio and video segment selection to As it is essential aspect to concentrate on metadata generate formatted video-based compositions on-the-fly. requirements while analyzing video contents, the authors To simplify video editing using metadata, the authors, Juan Werner Bailer and Martin [8], reviewed the metadata Casares, A. Chris Long, Brad A. Myers, Rishi Bhatnagar, Scott requirements for multi-view video content and analyze how M. Stevens, Laura Dabbish, Dan Yocum, and Albert Corbett well these requirements are covered in existing metadata [4], developed a system called Silver. The proposed system standards, both in terms of the coverage of metadata elements Silver shows that, video metadata can be used to create an and the capabilities to structurally describe multi-view video advanced user interface by providing an editable transcript content. view; coordinating the selection across all views including partial selections and different selections in audio and video; 3. METADATA FOR WEB VIDEOS and smart editing through smart snap, smart selection and In recent times there has been an explosion in the number of smart cut and paste. Silver uses video and metadata from the online videos. With the gradually increasing multimedia Informedia Digital Video Library. content, the task of efficient query-based video retrieval has There have been observed that, there exists insufficient and become important. The proper genre or category identification inappropriate for content description of complex data types of the video is essential for this purpose. The automatic genre such as videos, which require more detailed relational models. identification of videos has been traditionally posed as a For this, the authors D. M. Shotton, A. Rodriguez, N. Guil and supervised classification task of the features derived from the O. Trelles [5], proposed a metadata classification schema for audio, visual content and textual features, whereas some works the characterization of items and events in videos that permits focus on classifying the video based on the metadata (text) subsequent query by content. With respect to MPEG-7 provided by the uploader, other works attempt to extract low- nomenclature, metadata intrinsic to the information content of level features by analyzing the frames, signals, audio etc, along the video are defined as either structural or semantic, where with textual features. There have been some recent advances in structural metadata are numerical feature primitives produced incorporating new features for classification like the social by analyzing the color, shape, texture, structure and motion content comprising of the user connectivity, comments, interest within the video frames, whereas semantic metadata describe etc. Therefore, to classify or categorize the web video the locations and timings of individual items and particular effectively and efficiently, it is essential to analyze the actions or events in the video, and are thus of higher descriptive metadata given by the uploader, website metadata information value. In this aspect, the semantic metadata like comments, tags, etc, and technical metadata. In this required to describe the visual information content of videos section web video metadata classes and repository model is are defined and