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The Task-Dependent Effect of Tags and Ratings on Social Media access

ACM TRANSACTIONS ON INFORMATION systems 2010年第4期28卷 21.1-21.42页

作者： Clements, Maarten De Vries, Arjen P. Reinders, Marcel J. T. Delft Univ Technol Fac Elect Engn Math & Comp Sci EEMCS NL-2600 AA Delft Netherlands

Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings. Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems. Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr.

关键词： algorithms Experimentation Human Factors Personalization content retrieval recommendation social media random walk rating tagging librarything movielens

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Audio scene segmentation for video with generic content

Audio scene segmentation for video with generic content

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Conference on multimedia content access - algorithms and systems II

作者： Niu, Feng Goela, Naveen Divakaran, Ajay Abdel-Mottaleb, Mohamed Univ Miami Dept Elect & Comp Engn 1251 Mem Dr Coral Gables FL 33146 USA Mitsubishi Elect Res Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819469922

In this paper, we present a content-adaptive audio texture based method to segment video into audio scenes. The audio scene is modeled as a semantically consistent chunk of audio-data. Our algorithm is based on '' semantic audio texture analysis.'' At first, we train GMM models for basic audio classes such as speech, music, etc. Then we define the semantic audio texture based-on those classes. We study and present two types of scene changes, those corresponding to an overall audio-texture change and those corresponding to a-special '' transition marker '' used by the content creator, such as a short stretch of music in a sitcom or silence in dramatic content. Unlike prior work using genre specific heuristics, such as some methods presented for detecting commercials, we adaptively find out if such special transition markers are being used and if so, which of the base classes are being used as markers without any prior knowledge about the content. Our experimental results show that our proposed audio scene segmentation works well across a wide variety of broadcast content genres.

关键词： audio scene segmentation semantic texture SVM

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Improving multimedia retrieval with a video OCR

Improving multimedia retrieval with a video OCR

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Conference on multimedia content access - algorithms and systems II

作者： Das, Dipanjan Chen, Datong Hauptmann, Alexander G. Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9780819469922

We present a set of experiments with a video OCR system (VOCR) tailored for video information retrieval and establish its importance in multimedia search in general and for some specific queries in particular. The system, inspired by an existing work on text detection and recognition in images, has been developed using, techniques involving detailed analysis of video frames producing candidate text regions. The text regions are then binarized and sent to a commercial OCR resulting in ASCII text, that is finally used to create search indexes. The system is evaluated using the TREVID data.. We compare the system's performance from an information retrieval perspective with another VOCR developed, using multi-frame integration and empirically demonstrate that deep analysis on individual video frames result in better video retrieval. We also evaluate the effect of various textual sources on multimedia retrieval by combining the VOCR outputs with automatic speech recognition (ASR) transcripts. For general search queries, the VOCR system coupled with ASR sources outperforms the other system by a very large extent. For search queries that involve named entities, especially people names, the VOCR system even outperforms speech transcripts, demonstrating that source selection for particular query types is extremely essential.

关键词： video OCR OCR multimedia retrieval video retrieval optical character recognition

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Edit while watching: home video editing made easy

Edit while watching: home video editing made easy

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Conference on multimedia content access - algorithms and systems

作者： Campanella, Marco Weda, Hans Barbieri, Mauro Philips Res Europe High Tech Campus 34 NL-5656 AE Eindhoven Netherlands

ISBN: (纸本)9780819466198

In recent years, more and more people capture their experiences in home videos. However, home video editing still is a difficult and time-consuming task. We present the Edit While Watching system that allows users to automatically create and change a summary of a home video in an easy, intuitive and lean-back way. Based on content analysis, video is indexed, segmented, and combined with proper music and editing effects. The result is an automatically generated home video summary that is shown to the user. While watching it, users can indicate whether they like certain content, so that the system will adapt the summary to contain more content that is similar or related to the displayed content. During the video playback users can also modify and enrich the content, seeing immediately the effects of their changes. Edit While Watching does not require a complex user interface: a TV and a few keys of a remote control are sufficient. A user study has shown that it is easy to learn and to use, even if users expressed the need for more control in the editing operations and in the editing process.

关键词： automatic video editing content analysis film grammar video summarization home video summarization smart user interaction

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Know Your Data: Understanding Implicit Usage versus Explicit Action in Video content Classification

Know Your Data: Understanding Implicit Usage versus Explicit...

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Conference on multimedia on Mobile Devices and multimedia content access - algorithms and systems V

作者： Yew, Jude Shamma, David A. Yahoo Res Santa Clara CA 95054 USA

ISBN: (纸本)9780819484185

In this paper, we present a method for video category classification using only social metadata from websites like YouTube. In place of content analysis, we utilize communicative and social contexts surrounding videos as a means to determine a categorical genre, e. g. Comedy, Music. We hypothesize that video clips belonging to different genre categories would have distinct signatures and patterns that are reflected in their collected metadata. In particular, we define and describe social metadata as usage or action to aid in classification. We trained a Naive Bayes classifier to predict categories from a sample of 1,740 YouTube videos representing the top five genre categories. Using just a small number of the available metadata features, we compare the classifications produced by our Naive Bayes classifier with those provided by the uploader of that particular video. Compared to random predictions with the YouTube data (21% accurate), our classifier attained a mediocre 33% accuracy in predicting video genres. However, we found that the accuracy of our classifier significantly improves by nominal factoring of the explicit data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos. We argue that the patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. The results presented by this project represents a first step in investigating the potential meaning and significance of social metadata and its relation to the media experience.

关键词： Video Social Categorization YouTube Naive Bayes Classification

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Dual watermarking framework for privacy protection and content authentication of multimedia

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 94卷 654-673页

作者： Hurrah, Nasir N. Parah, Shabir A. Loan, Nazir A. Sheikh, Javaid A. Elhoseny, Mohammad Muhammad, Khan Univ Kashmir Post Grad Dept Elect & Instrumentat Technol Srinagar 190006 Jammu & Kashmir India Mansoura Univ Fac Comp & Informat El Gomhouria St Mansoura 35516 Egypt Sejong Univ Digital Contents Res Inst Intelligent Media Lab Seoul South Korea

In the current multimedia networked infrastructure, privacy breaches due to cyber-attacks result in huge economic losses. Despite these threats there is ever increasing demand to share data over various insecure networks for accomplishment of numerous tasks. In such a scenario there is a greater need to develop new algorithms for strengthening the existing cybersecurity frameworks, ensure security, privacy, copyright protection and authentication of data. In this paper a new technique for copyright protection, data security and content authentication of multimedia images is presented. The copyright protection of the media is taken care of by embedding a robust watermark using an efficient inter-block coefficient differencing algorithm and is proposed as Scheme I. Scheme II has been utilized to ensure both copyright protection, and content authentication. The authentication of the content has been ensured by embedding a fragile watermark in spatial domain while as copyright protection has been taken care of utilizing a robust watermark. In order to thwart an adversary and ensure that it has no access to actual embedded data, we make use of a novel encryption algorithm in conjunction with Arnold transform to encrypt data prior to its embedding. The experimental results reveal that the proposed framework offers high degree of robustness against single/dual/triple attacks;with Normalized Correlation (NCC), more than or equal to 0.95. Besides, the fragile watermark embedding makes the system capable of tamper detection and localization with average BER more than 45% for all signal processing/geometric attacks. The average PSNR achieved for both schemes is greater than 41 dB. A comparison of the proposed framework with various state-of-the-art techniques demonstrate its effectiveness and superiority. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multimedia security Cyber-attack Cybersecurity Robustness Copyright protection content authentication

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content-based unconstrained color logo and trademark retrieval with Color Edge Gradient Co-occurrence Histograms

Content-based unconstrained color logo and trademark retriev...

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Conference on multimedia content access - algorithms and systems II

作者： Phan, Raymond Androutsos, Dimitrios Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9780819469922

In this paper, we present a logo and trademark retrieval system for unconstrained color image databases that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme. We introduce more accurate information to the, CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, in comparison to the simple color pixel difference classification of edges as seen in the CECH. Our proposed method is thus reliant on edge gradient information, and as such, we call this the Color Edge Gradient Co-occurrence Histogram (CEGCH). We use this as the main mechanism for our unconstrained color logo and trademark retrieval scheme. Results illustrate that the proposed retrieval system retrieves logos and trademarks with good accuracy, and outperforms the CECH object detection scheme with higher precision and recall.

关键词： Color Edge Gradient Co-occurrence Histogram (CEGCH) logo and trademark retrieval color edge detection pattern recognition object detection

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Standards compliant watermarking for access management

Standards compliant watermarking for access management

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Conference on multimedia systems and Applications IV

作者： Ratnakar, V Guleryuz, OG Epson Palo Alto Lab Palo Alto CA 94304 USA

ISBN: (纸本)0819442429

We present standards-compliant visible watermarking schemes for digital images and video in DCT-based compressed formats. The watermarked data is in the same compressed format as the original and can be viewed with standard tools and applications. Moreover, for most of the schemes presented, the watermarked data has exactly the same compressed size as the original. The watermark can be inserted and removed using a key for applications requiring content protection. The watermark application and removal algorithms are very efficient and exploit some features of compressed data formats (such as JPEG and MPEG) which allow most of the work to be done in the compressed domain.

关键词： visible watermarks scrambler discrete cosine transform JPEG MPEG content protection

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Semantic storyboard of judicial debates: a novel multimedia summarization environment

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PROGRAM-ELECTRONIC LIBRARY AND INFORMATION systems 2012年第2期46卷 199-219页

作者： Fersini, E. Sartori, F. Univ Milano Bicocca DISCo Milan Italy

Purpose - The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues. Design/methodology/approach - In this context the JUMAS system, stem from the homonymous European Project (***), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders. Findings - In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem. Originality/value - The adoption of ontology-based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings.

关键词： multimedia summarization Ontological query expansion multimedia Justice Information retrieval

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Generic and optimized framework for multi-content analysis based on learning approaches

Generic and optimized framework for multi-content analysis b...

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Conference on Imaging and Printing in a Web 2.0 World;and multimedia content access - algorithms and systems IV

作者： Besnehard, Quentin Marchessoux, Cedric Kimpe, Tom Barco NV B-8500 Kortrijk Belgium

ISBN: (纸本)9780819479334

During the European Cantata project (ITEA project, 2006-2009), a Multi-content Analysis framework for the classification of compound images in various categories (text, graphical user interface, medical images, other complex images) was developed within Barco. The framework consists of six parts: a dataset, a feature selection method, a machine learning based Multi-content Analysis (MCA) algorithm, a Ground Truth, an evaluation module based on metrics and a presentation module. This methodology was built on a cascade of decision tree-based classifiers combined and trained with the AdaBoost meta-algorithm. In order to be able to train these classifiers on large training datasets without excessively increasing the training time, various optimizations were implemented. These optimizations were performed at two levels: the methodology itself (feature selection / elimination, dataset pre-computation) and the decision-tree training algorithm (binary threshold search, dataset presorting and alternate splitting algorithm). These optimizations have little or no negative impact on the classification performance of the resulting classifiers. As a result, the training time of the classifiers was significantly reduced, mainly because the optimized decision-tree training algorithm has a lower algorithmic complexity. The time saved through this optimized methodology was used to compare the results of a greater number of different training parameters.

关键词： Multi-content analysis machine learning AdaBoost decision trees optimization

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