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Conference on multimedia on Mobile Devices ii

作者： Mariappan, A Igarta, M Taskiran, C Gandhi, B Delp, EJ Purdue Univ Video & Image Proc Lab VIPER Sch Elect & Comp Engn W Lafayette IN 47907 USA Motorola Labs Multimedia Res Lab Schaumburg IL USA

ISBN: (纸本)0819461148

With the proliferation of cameras in handheld devices that allows users to capture still images and videos, providing users with software tools to efficiently manage multimedia content has become essential. In many cases users desire to organize their personal media content using high-level semantic labels. In this paper we will describe low-complexity algorithms that can be used to derive semantic labels, such as "indoor/outdoor," "face/not face," and "motion/not motion" for mobile video sequences. We will also describe a method for summarizing mobile video sequences. We demonstrate the classification performance of the methods and their computational complexity using a typical processor used in many mobile terminals.

关键词： Video Skin Databases Facial recognition systems Semantic video Classification systems Image classification multimedia Binary data RGB color model

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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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Harvesting weakly-tagged images for computer vision tasks

Harvesting weakly-tagged images for computer vision tasks

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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： Shen, Yi Yang, Chunlei Gao, Yuli Fan, Jianping UNC Charlotte Dept Comp Sci Charlotte NC USA HP Labs Multimedia Interact & Understand Lab Palo Alto CA USA

ISBN: (纸本)9780819479334

To crawl large amounts of weakly-tagged images for computer vision tasks such as object detection and scene recognition, it is very important to develop new techniques for tag cleansing and word sense disambiguation (i.e., removing irrelevant images from the crawled results). Based on this observation, a topic network is first generated to characterize both the semantic similarity contexts and the visual similarity contexts between the image topics more sufficiently. The topic network is used to represent the classes of objects and scenes of interest. Second, both the visual similarity contexts between the images and the semantic similarity contexts between their tags are integrated for tag cleansing and word sense disambiguation. By addressing the issues of polysemes and synonyms more effectively, our word sense disambiguation algorithm can determine the relevance between the images and the associated tags more precisely, and thus it can allow us to crawl large-scale weakly-tagged images for computer vision tasks.

关键词： Weakly-tagged images tag cleansing topic network

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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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Hierarchical temporal video segmentation and content characterization

Hierarchical temporal video segmentation and content charact...

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Conference on multimedia Storage and Archiving systems ii

作者： Gunsel, B Fu, Y Tekalp, AM Univ. of Rochester (United States)

ISBN: (纸本)0819426628

This paper addresses the segmentation of a video sequence into shots, specification of edit effects and subsequent characterization of shots in terms of color and motion content. The proposed scheme uses DC images extracted from MPEG compressed video and performs an unsupervised clustering for the extraction of camera shots. The specification of edit effects, such as fade-in/out and dissolve is based on the analysis of distribution of mean value for the luminance components. This step is followed by the representation of visual content of temporal segments in terms of key frames selected by similarity analysis of mean color histograms. For characterization of the similar temporal segments, motion and color characteristics are classified into different categories using a set of different features derived from motion vectors of triangular meshes and mean histograms of video shots.

关键词： video databases content-based access shot detection edit effects content characterization

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Deep Learning-Based Detection of Inappropriate Speech content for Film Censorship

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IEEE access 2022年 10卷 101697-101715页

作者： Wazir, Abdulaziz Saleh Ba Karim, Hezerul Abdul Lyn, Hor Sui Fauzi, Mohammad Faizal Ahmad Mansor, Sarina Lye, Mohd Haris Multimedia Univ Fac Engn Cyberjaya 63000 Malaysia

Audible content has become an effective tool for shaping one's personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos and films that are likely to contain an inappropriate speech content. With this in view, all the broadcasting and online video/audio platform companies hire a lot of manpower to detect the foul voices prior to censorship. The process has a large cost in terms of manpower, time and financial resources. In addition to inaccurate detection of foul voices due to fatigue of manpower and weakness of human visual and hearing system in long time and monotonous tasks. As such, this paper proposes an intelligent deep learning-based system for film censorship through a fast and accurate detection and localization approach using advanced deep Convolutional Neural Networks (CNNs). The dataset of foul language containing isolated words samples and continuous speech were collected, annotated, processed, and analyzed for the development of automated detection of inappropriate speech content. The results indicated the feasibility of the suggested systems by reporting a high volume of inappropriate spoken terms detection. The proposed system outperformed state-of-the-art baseline algorithms on the novel foul language dataset evaluation metrics in terms of macro average AUC (93.85%), weighted average AUC (94.58%), and all other metrics such as F1-score. Additionally, proposed acoustic system outperformed ASR-based system for profanity detection based on the evaluation metrics including AUC, accuracy, precision, and F1-score. Additionally, proposed system was proven to be faster than human manual screening and detection of audible content for films' censorship.

关键词： Speech recognition Acoustics Computational modeling Deep learning Motion pictures Censorship Speech processing Foul language speech recognition key word spotting spoken term detection censorship deep learning convolutional neural network

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Multi-module human motion analysis from a monocular video

Multi-module human motion analysis from a monocular video

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Conference on multimedia content access - algorithms and systems

作者： Lao, Weilun Han, Jungong de With, Peter H. N. Eindhoven Univ Technol POB 513 NL-5600 MB Eindhoven Netherlands LogicaCMG Netherlands NL-5600 JB Eindhoven Netherlands

ISBN: (纸本)9780819466198

In this paper, we propose an effective framework for semantic analysis of human motion from a monocular video. As it is difficult to find a good motion description for humans, we focus on a reliable recognition of the motion type and estimate the body orientation involved in the video sequence. Our framework analyzes the body motion in three modules: a pre-processing module, matching module and semantic module. The proposed framework includes novel object-level processing algorithms, such as a local descriptor and a global descriptor to detect body parts and analyze the shape of the whole body as well. Both descriptors jointly contribute to the matching process by incorporating them into a new weighted linear combination for matching. We also introduce a simple cost function based on time-index differences to distinguish motion types and cycles in human motions. Our system can provide three different types of analysis results: (1) foreground person detection;(2) motion recognition in the sequence;(3) 3-D modeling of human motion based on generic human models. The proposed framework was evaluated and proved its effectiveness as it achieves the motion recognition and body-orientation classification at the accuracy of 95 % and 98 %, respectively.

关键词： human motion content analysis 3-D modeling feature extraction

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Material Classification and Automatic content Enrichment of Images Using Supervised Learning and Knowledge Bases

Material Classification and Automatic Content Enrichment of ...

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

作者： Mallepudi, Sri Abhishikth Calix, Ricardo A. Knapp, Gerald M. Louisiana State Univ Baton Rouge LA 70803 USA

ISBN: (纸本)9780819484185

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e. g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

关键词： Image classification Binary data RGB color model Machine learning Image filtering Data modeling Feature extraction Databases Buildings content based image retrieval

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