Multimedia event detection with multimodal feature fusion and temporal concept localization

We present a system for multimedia event detection. The developed system characterizes complex multimedia events based on a large array of multimodal features, and classifies unseen videos by effectively fusing diverse responses. We present three major technical innovations. First, we explore novel visual and audio features across multiple semantic granularities, including building, often in an unsupervised manner, mid-level and high-level features upon low-level features to enable semantic understanding. Second, we show a novel Latent SVM model which learns and localizes discriminative high-level concepts in cluttered video sequences. In addition to improving detection accuracy beyond existing approaches, it enables a unique summary for every retrieval by its use of high-level concepts and temporal evidence localization. The resulting summary provides some transparency into why the system classified the video as it did. Finally, we present novel fusion learning algorithms and our methodology to improve fusion learning under limited training data condition. Thorough evaluation on a large TRECVID MED 2011 dataset showcases the benefits of the presented system.     

Tensor-Based Transductive Learning for Multimodality Video Semantic Concept Detection

Interaction and integration of multimodality media types such as visual, audio, and textual data in video are the essence of video semantic analysis. Contextual information propagation is useful for both intra- and inter-shot correlations. However, the traditional concatenated vector representation of videos weakens the power of the propagation and compensation among the multiple modalities. In this paper, we introduce a higher-order tensor framework for video analysis. We represent image frame, audio, and text in video shots as data points by the 3rd-order tensor. Then we propose a novel dimension reduction algorithm which explicitly considers the manifold structure of the tensor space from contextual temporal associated cooccurring multimodal media data. Our algorithm inherently preserves the intrinsic structure of the sub- manifold where tensorshots are sampled and is also able to map out-of-sample data points directly. We propose a new transductive support tensor machines algorithm to train effective classifier using large amount of unlabeled data together with the labeled data. Experiment results on TREVID 2005 data set show that our method improves the performance of video semantic concept detection.     

Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.     

Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.     

Video data mining: semantic indexing and event detection from the association perspective

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.     

A preliminary study of combining mass spectrometric data with audio and video signals for real-time monitoring of controlled lab-scale fires

The different aspects of sensors integration, and specifically that of a Mass Spectrometer (MS) with audio and video signals, are investigated for detecting and monitoring indoor fire events. The present study focuses on comparing the capabilities of a variety of chemical sensors, on answering technical challenges in regard to the integration of chemical, audio and video signals and on discussing integration issues for potential field applications. Controlled, small scale fire experiments were carried out in the laboratory. A commercial MS coupled with an in-house developed Pulsed Sampling System (PSS), was used for on-line sampling and near real-time monitoring of the evolved volatiles. The detection limit of PSS-MS was found to be 150 ppbv and its linearity was confirmed up to 10 ppmv using benzene gas standards. The profiles of ions with m/z 57, 78, 91 and 106, corresponding to indicative Volatile Organic Compounds (VOCs) of the fire event, were recorded and compared with the concentration profiles of CO₂, CO, O₂, NO and H/C (C₃H₈), acquired by the gas sensors of a commercial exhaust gas analyzer. Audio and video signals were recorded by a microphone and a visual camera, simultaneously, with PSS-MS data. Two types of fire experiments were performed in order to simulate field conditions: (a) direct fire monitoring, in case of unobstructed direct fire view and (b) indirect fire monitoring through reflection of audio and video signals on metallic surfaces, for simulating obstacles preventing direct fire view. The information derived by audio and video signals reaffirmed the chemical detection inferences for both types of fire experiments, thus increasing the credibility of each individual method. Occasionally, video, audio and chemical information were complementary, thus counterbalancing the detection limitations of the individual methods. The integrated approach of combining MS data with audio and video signals appears to be a promising method in safety and security applications, where reliable, early detection and real-time monitoring is necessary.     

Multi-label semantic concept detection in videos using fusion of asymmetrically trained deep convolutional neural networks and foreground driven concept co-occurrence matrix

Describing visual contents in videos by semantic concepts is an effective and realistic approach that can be used in video applications such as annotation, indexing, retrieval and ranking. In these applications, video data needs to be labelled with some known set of labels or concepts. Assigning semantic concepts manually is not feasible due to the large volume of ever-growing video data. Hence, automatic semantic concept detection of videos is a hot research area. Recently Deep Convolutional Neural Networks (CNNs) used in computer vision tasks are showing remarkable performance. In this paper, we present a novel approach for automatic semantic video concept detection using deep CNN and foreground driven concept co-occurrence matrix (FDCCM) which keeps foreground to background concept co-occurrence values, built by exploiting concept co-occurrence relationship in pre-labelled TRECVID video dataset and from a collection of random images extracted from Google Images. To deal with the dataset imbalance problem, we have extended this approach by making a fusion of two asymmetrically trained deep CNNs and used FDCCM to further improve concept detection. The performance of the proposed approach is compared with state-of-the-art approaches for the video concept detection over the widely used TRECVID data set and is found to be superior to existing approaches.     

A framework for improved video text detection and recognition

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.     

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

With the advance of digital video recording and playback systems, the request for efficiently managing recorded TV video programs is evident so that users can readily locate and browse their favorite programs. In this paper, we propose a multimodal scheme to segment and represent TV video streams. The scheme aims to recover the temporal and structural characteristics of TV programs with visual, auditory, and textual information. In terms of visual cues, we develop a novel concept named program-oriented informative images (POIM) to identify the candidate points correlated with the boundaries of individual programs. For audio cues, a multiscale Kullback-Leibler (K-L) distance is proposed to locate audio scene changes (ASC), and accordingly ASC is aligned with video scene changes to represent candidate boundaries of programs. In addition, latent semantic analysis (LSA) is adopted to calculate the textual content similarity (TCS) between shots to model the inter-program similarity and intra-program dissimilarity in terms of speech content. Finally, we fuse the multimodal features of POIM, ASC, and TCS to detect the boundaries of programs including individual commercials (spots). Towards effective program guide and attracting content browsing, we propose a multimodal representation of individual programs by using POIM images, key frames, and textual keywords in a summarization manner. Extensive experiments are carried out over an open benchmarking dataset TRECVID 2005 corpus and promising results have been achieved. Compared with the electronic program guide (EPG), our solution provides a more generic approach to determine the exact boundaries of diverse TV programs even including dramatic spots.     

Everyday concept detection in visual lifelogs: validation,relationships and trends

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user’s day-to-day activities. It captures on average 3,000 images in a typical day, equating to almost 1 million images per year. It can be used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer’s life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the domain of visual lifelogs. Our concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept’s presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were evaluated on a subset of 95,907 images, to determine the accuracy for detection of each semantic concept. We conducted further analysis on the temporal consistency, co-occurance and relationships within the detected concepts to more extensively investigate the robustness of the detectors within this domain.     

Development of Industry 4.0 predictive maintenance architecture for broadcasting chain

Industry 4.0 Predictive Maintenance (PdM 4.0) architecture in the broadcasting chain is one of the taxonomy challenges for deploying Industry 4.0 frameworks. This paper proposes a novel PdM framework based on advanced Reference Architecture Model Industry 4.0 (RAMI 4.0) to reduce operation and maintenance costs. This framework includes real-time production monitoring, business processes, and integration based on Design Science Research (DSR) to generate an innovative Business Process Model and Notation (BPMN) meta-model. The addressed model visualizes sub-processes based on experts' and stakeholders' knowledge to reduce the cost of maintenance of audiovisual services including satellite TV, cable TV, and live audio and video broadcast services. Based on the recommendation and the concept of Industry 4.0, the proposed framework tolerates the predictable failures and further concerns in similar related industries. Some empirical experiments have been conducted by using the Islamic Republic of Iran Broadcasting’s (IRIB) high-power station (located near the capital city of Iran, Tehran) to evaluate the functionality and efficiency of the proposed predictive maintenance framework. Practical outcomes demonstrate that interval times between data collection should be increased in audio and video broadcasting predictive maintenance because of the limitation of the internal processing performance of equipment. The framework also indicates the role of the Frequency Modulation (FM) transmitters’ data clearance to reduce the instability and untrustworthy data during data mining. The proposed DSR method endorses using a customized RAMI 4.0 meta-model framework to adapt distributed broadcasting and communication with PdM 4.0, which increases the stability as well as decreasing maintenance costs of the broadcasting chain in comparison to state-of-the-art methodologies. Furthermore, it is shown that the proposed framework outperforms the best-evaluated methods in terms of acceptance.     

The semantic pathfinder: using an authoring metaphor for generic multimedia indexing

This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives.     

Retina enhanced SURF descriptors for spatio-temporal concept detection

This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static images to video sequences exhibits some new problems to cope with, mainly the way to use the temporal information related to the concepts to detect (swimming, drinking...). In this study, we propose to apply a human retina model to preprocess video sequences before constructing the State-Of-The-Art BoW analysis. This preprocessing, designed in a way that enhances relevant information, increases the performance by introducing robustness to traditional image and video problems, such as luminance variation, shadows, compression artifacts and noise. Additionally, we propose a new segmentation method which enables a selection of low-level spatio-temporal potential areas of interest from the visual scene, without slowing the computation as much as a high-level saliency model would. These approaches are evaluated on the TrecVid 2010 and 2011 Semantic Indexing Task datasets, containing from 130 to 346 high-level semantic concepts. We also experiment with various parameter settings to check their effect on performance.     

基于文本及视音频多模态信息的新闻分割

提出了一种融合文本和视音频多模态特征的电视新闻自动分割方案。该方案充分考虑各种媒体特征的特点,先用矢量模型和GMM对文本进行预分割,用语谱图和HMM对语音预分割、用改进的直方图和SVM分类器对视频进行预分割。然后在时间同步的基础上,使用复合策略用ANN对预分割的数据进行融合,从而获得具有一定语义内容的视频段。实验结果表明此方法的有效性,并且分割后的视频片段具备较完整的语义信息特征,避免了分割的过度细碎的弊端。