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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： Cusano, Claudio Santini, Simone Univ Milano Bicocca Viale Sarca 336 I-20126 Milan Italy Univ Autonoma Madrid E-28049 Madrid Spain

ISBN: (纸本)9780819479334

We propose a method the for semi-automatic organization of photo albums. The method analyzes how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or made a correction. We use a suitable feature representation of the images to model the different classes that the users have collected. Then, we look for correspondences between the criteria used by the different users which are integrated using boosting. A quantitative evaluation of the proposed approach is obtained by simulating the amount of user interaction needed to annotate the albums of a set of members of the flickr (R) photo-sharing community.

关键词： Personal photography automatic image annotation content-based image analysis social image retrieval

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MoCA workbench: Support for creativity in movie content analysis

MoCA workbench: Support for creativity in movie content anal...

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Proceedings of the 1996 International Conference on multimedia Computing and systems

作者： Lienhart, Rainer Pfeiffer, Silvia Effelsberg, Wolfgang Univ of Mannheim Mannheim Germany

Semantic access to the content of a video is highly desirable for multimedia content retrieval. Automatic extraction of semantics requires content analysis algorithms. Our MoCA (Movie content Analysis) project provides an interactive workbench supporting the researcher in the development of new movie content analysis algorithms. The workbench offers data management facilities for large amounts of video/audio data and derived parameters. It also provides an easy-to-use interface for a free combination of basic operators into more sophisticated operators. We can combine results from video track and audio track analysis. The MoCA Workbench shields the researcher from technical details and provides advanced visualization capabilities, allowing attention to focus on the development of new algorithms. The paper presents the design and implementation of the MoCA Workbench and reports practical experience.

关键词： Video signal processing

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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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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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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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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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Semantic classification, low level features and relevance feedback for content-based image retrieval

Semantic classification, low level features and relevance fe...

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multimedia content access: algorithms and systems III

作者： Ciocca, G. Cusano, C. Schettini, R. DISCo Dipartimento di Informática Sistemistica e Comunicazione Universita degli Studi di Milano-Bicocca Viale Sarca 336 20126 Milano Italy

Although traditional content-based retrieval systems have been successfully employed in many multimedia applications, the need for explicit association of higher concepts to images has been a pressing demand from users. Many research works have been conducted focusing on the reduction of the semantic gap between visual features and the semantics of the image content. In this paper we present a mechanism that combines broad high level concepts and low level visual features within the framework of the QuickLook content-based image retrieval system. This system also implements a relevance feedback algorithm to learn users' intended query from positive and negative image examples. With the relevance feedback mechanism, the retrieval process can be efficiently guided toward the semantic or pictorial contents of the images by providing the system with the suitable examples. The qualitative experiments performed on a database of more than 46,000 photos downloaded from the Web show that the combination of semantic and low level features coupled with a relevance feedback algorithm, effectively improve the accuracy of the image retrieval sessions. © 2009 SPIE-IS&T.

关键词： Semantics

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Intelligent systems for video analysis and access over the Internet

Intelligent systems for video analysis and access over the I...

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作者： Zhou, Wensheng University of Southern California

学位级别：Ph.D.

This dissertation presents a solution to problems arising from the demand for fast information access and for sharing in real-time multimedia transmission over the Internet. Our solution exploits software agents that are placed throughout the network environment. These hierarchical video analysis agents process multimedia streams in real time, and automatically decompose and understand the multimedia content so as to facilitate information access and sharing. multimedia content contains both the perceptual content such as color, motion, or acoustic features and the conceptual content, which is specified based on concepts or semantics that can be expressed by text descriptions. Both types of contents are embedded simultaneously in multimedia streams, and usually are complementary to each other. This dissertation adaptively analyzes both kinds of video contents by combining mixed media cues from audio, video and text. First, a high-performance module for on-line video segmentation based on scene-change detection is developed. It serves as the first step of any video stream construction and analysis. To meet the high computational demand, our proposed video scene change detection algorithms are very efficient while maintaining high accuracy and recall rates for fast on-line video analysis. Second, the perceptual features of audio and video data are analyzed in a bottom-up manner and integrated so as to discriminate among the different events in any video stream effectively. An efficient decision-tree learning algorithm is used to induce a set of if-then rules which link perceptual features with the video conceptual semantic contents. These rules not only serve as a video classifier, but also guide on-line real-time video/audio feature extraction and data redistribution. A novel knowledge-based system, where knowledge is stored as learned rules, is proposed to serve as a video semantic inference/classification engine. Third, we propose a hierarchical video categorizatio

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