Query: Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups

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Automatic Acquisition of Hyponyms from Large Text Corpora <=SEP=> We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexicosyntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.

Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis <=SEP=> The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10-13% and 3-5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.

Clash of the Contagions: Cooperation and Competition in Information Diffusion <=SEP=> In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network. While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other's probability of spreading, while cooperating contagions help each other in being adopted throughout the network. We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model learns how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by 71% on the average.

Patterns of temporal variation in online media <=SEP=> Online content exhibits rich temporal dynamics, and diverse realtime user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored.
 We study temporal patterns associated with online content and how the content's popularity grows and fades over time. The attention that content receives on the Web varies depending on many factors and occurs on very different time scales and at different resolutions. In order to uncover the temporal dynamics of online content we formulate a time series clustering problem using a similarity metric that is invariant to scaling and shifting. We develop the K-Spectral Centroid (K-SC) clustering algorithm that effectively finds cluster centroids with our similarity measure. By applying an adaptive wavelet-based incremental approach to clustering, we scale K-SC to large data sets.
 We demonstrate our approach on two massive datasets: a set of 580 million Tweets, and a set of 170 million blog posts and news media articles. We find that K-SC outperforms the K-means clustering algorithm in finding distinct shapes of time series. Our analysis shows that there are six main temporal shapes of attention of online content. We also present a simple model that reliably predicts the shape of attention by using information about only a small number of participants. Our analyses offer insight into common temporal patterns of the content on theWeb and broaden the understanding of the dynamics of human attention.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews <=SEP=> This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

An annotation tool for automatically triangulating individuals' psychophysiological emotional reactions to digital media stimuli <=SEP=> Current affective user experience studies require both laborious and time-consuming data analysis, as well as dedicated affective classification algorithms. Moreover, the high technical complexity and lack of general guidelines for developing these affective classification algorithms further limits the comparability of the obtained results. In this paper we target this issue by presenting a tool capable of automatically annotating and triangulating players' physiologically interpreted emotional reactions to in-game events. This tool was initially motivated by an experimental psychology study regarding the emotional habituation effects of audio-visual stimuli in digital games and we expect it to contribute in future similar studies by providing both a deeper and more objective analysis on the affective aspects of user experience. We also hope it will contribute towards the rapid implementation and accessibility of this type of studies by open-sourcing it. Throughout this paper we describe the development and benefits presented by our tool, which include: enabling researchers to conduct objective a posteriori analyses without disturbing the gameplay experience, automating the annotation and emotional response identification process, and formatted data exporting for further analysis in third-party statistical software applications.

A 3-D printed lightweight cavity-backed monocone antenna <=SEP=> A lightweight exponentially tapered cavity-backed mono-cone antenna made by 3-D printing and conductive paint coating is proposed. To make the 3-D printed structure be metalized, three layers of conductive paint are used. To verify the proposed fabrication method, the 3-D printed antenna was fabricated and its return loss and far-field radiation characteristics were analyzed.

Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images <=SEP=> Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate “salt and pepper” phenomena and block effects.

Generating Neural Networks with Neural Networks <=SEP=> Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by parameter sharing. We perform experiments and show that the generated weights are diverse and lie on a non-trivial manifold.

OPTIMIZING THE PERFORMANCE OF VEHICLE-TO-GRID ( V 2 G ) ENABLED BATTERY ELECTRIC VEHICLES THROUGH A SMART CHARGE SCHEDULING MODEL <=SEP=> −A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV

Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships <=SEP=> The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.

MC-HOG Correlation Tracking with Saliency Proposal <=SEP=> Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.

Leveraging stereopsis for saliency analysis <=SEP=> Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer's attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.

The line of action: an intuitive interface for expressive character posing <=SEP=> The line of action is a conceptual tool often used by cartoonists and illustrators to help make their figures more consistent and more dramatic. We often see the expression of characters---may it be the dynamism of a super hero, or the elegance of a fashion model---well captured and amplified by a single aesthetic line. Usually this line is laid down in early stages of the drawing and used to describe the body's principal shape. By focusing on this simple abstraction, the person drawing can quickly adjust and refine the overall pose of his or her character from a given viewpoint. In this paper, we propose a mathematical definition of the line of action (LOA), which allows us to automatically align a 3D virtual character to a user-specified LOA by solving an optimization problem. We generalize this framework to other types of lines found in the drawing literature, such as secondary lines used to place arms. Finally, we show a wide range of poses and animations that were rapidly created using our system.

Multi-view point clouds registration and stitching based on SIFT feature <=SEP=> In order to solve multi-view point clouds registration in large non-feature marked scenes, a new registration and stitching method was proposed based on 2D SIFT(Scale-invariant feature transform) features. Firstly, we used texture mapping method to generate 2D effective texture image, and then extracted and match SIFT features, obtained accurate key points and registration relationship between the effective texture images. Secondly, we reflected the SIFT key points and registration relationship to the 3D point clouds data, obtained key points and registration relationship of multi-view point clouds, we can achieve multi-view point clouds stitching. Our algorithm used texture mapping method to generate 2D effective texture image, it can eliminate interference of the holes and ineffective points, and can eliminate unnecessary mistake matching. Our algorithm used correct extracted matching point pairs to stitch, avoiding stepwise iterated of ICP algorithm, so our algorithm is simple to calculate, and it's matching precision and matching efficiency are improved to some extent. We carried experiments on two-view point clouds in two large indoor; the experiment results verified the validity of our algorithm.

An IOT based human healthcare system using Arduino uno board <=SEP=> Internet of Things (IOT) visualizes a future of anything anywhere by anyone at any time. The Information and communication technologies help in creating a revolution in digital technology. IOT are known for interconnecting various physical devices with the networks. In IOT, various physical devices are embedded with different types of sensors and other devices to exchange data between them. An embedded system is a combination of software and hardware where they are programmed for functioning specific functions. These data can be accessed from any parts of the world by making use of cloud. This can be used for creating a digital world, smart homes, healthcare systems and real life data exchange like smart banking. Though Internet of things has emerged long back, it is now becoming popular and gaining attention lately. In healthcare industry, some of the hospitals started using sensors implemented in the bed to get the status of patient's movement and other activities. This paper contains various IOT applications and the role of IOT in the healthcare system, challenges in the healthcare system using IOT. Also, introduced a secured surveillance monitoring system for reading and storing patient's details using low power for transmitting the data.

Beyond Cybersecurity Awareness: Antecedents and Satisfaction <=SEP=> Organizations develop technical and procedural measures to protect information systems. Relying only on technical based security solutions is not enough. Organizations must consider technical security solutions along with social, human, and organizational factors. The human element represents the employees (insiders) who use the information systems and other technology resources in their day-to-day operations. ISP awareness is essential to protect organizational information systems. This study adapts the Innovation Diffusion Theory to examine the antecedents of ISP awareness and its impact on the satisfaction with ISP and security practices. A sample of 236 employees in universities in the United States is collected to evaluate the research model. Results indicated that ISP quality, self-efficacy, and technology security awareness significantly impact ISP awareness. The current study presents significant contributions toward understanding the antecedents of ISP awareness and provides a starting point toward including satisfaction aspect in information security behavioral domain.

Vehicle detection and classification based on convolutional neural network <=SEP=> Deep learning has emerged as a hot topic due to extensive application and high accuracy. In this paper this efficient method is used for vehicle detection and classification. We extract visual features from the activation of a deep convolutional network, large-scale sparse learning and other distinguishing features in order to compare their accuracy. When compared to the leading methods in the challenging ImageNet dataset, our deep learning approach obtains highly competitive results. Through the experiments with in short of training data, the features extracted by deep learning method outperform those generated by traditional approaches.

Neuroticism as a mediator of treatment response to SSRIs in major depressive disorder. <=SEP=> BACKGROUND
Serotonin function has been implicated in both major depressive disorder and neuroticism. In the current investigation, we examined the hypothesis that any change in depression severity is mediated through the reduction of neuroticism, but only for those compounds which target serotonin receptors.


METHODS
Ninety-three outpatients in the midst of a major depressive episode received one of three antidepressant medications, classified into two broad types: selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs (i.e. reversible monoamine oxidase inhibitors [RIMAs] and noradrenergic and dopaminergic reuptake blockers [NDMs]). Patients completed the Hamilton Rating Scale for Depression, Beck Depression Inventory II and Revised NEO Personality Inventory prior to and following approximately 16 weeks of treatment. Structural equation modeling was used to test two models: a mediation model, in which neuroticism change is the mechanism by which SSRIs exert a therapeutic effect upon depressive symptoms, and a complication model, in which neuroticism change is a mere epiphenomenon of depression reduction in response to SSRIs.


RESULTS
The mediation model provided a good fit to the data; the complication model did not. Patients treated with SSRIs demonstrated greater neuroticism change than those treated with non-SSRIs, and greater neuroticism change was associated with greater depressive symptom change. These effects held for both self-reported and clinician-rated depressive symptom severity.


LIMITATIONS
Replication within a randomized control trial with multiple assessment periods is required.


CONCLUSION
Neuroticism mediates changes in depression in response to treatment with SSRIs, such that any treatment effect of SSRIs occurs through neuroticism reduction.

Voice as sound: using non-verbal voice input for interactive control <=SEP=> We describe the use of non-verbal features in voice for direct control of interactive applications. Traditional speech recognition interfaces are based on an indirect, conversational model. First the user gives a direction and then the system performs certain operation. Our goal is to achieve more direct, immediate interaction like using a button or joystick by using lower-level features of voice such as pitch and volume. We are developing several prototype interaction techniques based on this idea, such as "control by continuous voice", "rate-based parameter control by pitch," and "discrete parameter control by tonguing." We have implemented several prototype systems, and they suggest that voice-as-sound techniques can enhance traditional voice recognition approach.

Improving Diversity in Ranking using Absorbing Random Walks <=SEP=> We introduce a novel ranking algorithm called GRASSHOPPER, which ranks items with an emphasis on diversity. That is, the top items should be different from each other in order to have a broad coverage of the whole item set. Many natural language processing tasks can benefit from such diversity ranking. Our algorithm is based on random walks in an absorbing Markov chain. We turn ranked items into absorbing states, which effectively prevents redundant items from receiving a high rank. We demonstrate GRASSHOPPER’s effectiveness on extractive text summarization: our algorithm ranks between the 1st and 2nd systems on DUC 2004 Task 2; and on a social network analysis task that identifies movie stars of the world.

Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples <=SEP=> In recent years, deep learning has shown performance breakthroughs in many prominent problems. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. Adversarial examples are modified inputs designed to cause the model to err. In the domains of images and speech, the modifications are untraceable by humans, but affect the model’s output. In binary files, small modifications to the byte-code often lead to significant changes in its functionality/validity, therefore generating adversarial examples is not straightforward. We introduce a novel approach for generating adversarial examples for discrete input sets, such as binaries. We modify malicious binaries by injecting a small sequence of bytes to the binary file. The modified files are detected as benign, while preserving their malicious functionality. We applied this approach to an end-to-end convolutional neural malware detector and present a high evasion rate. Moreover, we showed that our payload is transferable within different positions of the same file and across different files and file families.

Intrusion Detection System using K-means , PSO with SVM Classifier : A Survey <=SEP=> Intrusion detection is a process of identifying the Attacks. The main aim of IDS is to identify the Normal and Intrusive activities. In recent years, many researchers are using data mining techniques for building IDS. Here we propose a new approach using data mining technique such as SVM and Particle swarm optimization for attaining higher detection rate. The propose technique has major steps: Preprocessing, Training using PSO, clustering using K-means to generate different training subsets. Then based on the subsequent training subsets a vector for SVM classification is formed and in the end, classification using PSO is performed to detect Intrusion has happened or not. This paper contains summarization study and identification of the drawbacks of formerly surveyed works. Keywords-Intrusion detection system; Neuro-fuzzy; Support Vector Machine (SVM); PSO; K-means

Artificial Life and Real Robots <=SEP=> The first part of this paper explores the general issues in using Artificial Life techniques to program actual mobile robots. In particular it explores the difficulties inherent in transferring programs evolved in a simulated environment to run on an actual robot. It examines the dual evolution of organism morphology and nervous systems in biology. It proposes techniques to capture some of the search space pruning that dual evolution offers in the domain of robot programming. It explores the relationship between robot morphology and program structure, and techniques for capturing regularities across this mapping. The second part of the paper is much more specific. It proposes techniques which could allow realistic explorations concerning the evolution of programs to control physically embodied mobile robots. In particular we introduce a new abstraction for behavior-based robot programming which is specially tailored to be used with genetic programming techniques. To compete with hand coding techniques it will be necessary to automatically evolve programs that are one to two orders of magnitude more complex than those previously reported in any domain. Considerable extensions to previously reported approaches to genetic programming are necessary in order to achieve this goal.

VARIATIONAL RECURRENT ADVERSARIAL DEEP DOMAIN ADAPTATION <=SEP=> We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex temporal relationships that are domain-invariant. This is (as far as we know) the first to capture and transfer temporal latent dependencies of multivariate time-series data. Through experiments on real-world multivariate healthcare time-series datasets, we empirically demonstrate that learning temporal dependencies helps our model’s ability to create domain-invariant representations, allowing our model to outperform current state-of-the-art deep domain adaptation approaches.

Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms <=SEP=> * Archer's capability of attention and relaxation control during shooting process was evaluated using EEG technology. Attention and meditation algorithms were used to represent the levels of mental concentration and relaxation levels. Elite, mid-level, and novice archers were tested for short and long distance shootings in the archery field. Single channel EEG was recorded on the forehead (Fp1) during the shooting process, and attention and meditation levels were computed by real time. Four types of variations were defined based on the increasing and decreasing patterns of attention and meditation levels during shooting process. Elite archers showed increases in both attention and relaxation while mid-level archers showed increased attention but decreased relaxation. Elite archers also showed higher levels of attention at the release than mid-level and novice archers. Levels of attention and relaxation and their variation patterns were useful to categorize archers and to provide feedback in training.

Analysis of the Electromagnetic Signature of Reinforced Concrete Structures for Nondestructive Evaluation of Corrosion Damage <=SEP=> This paper presents a nondestructive corrosion damage detection method for reinforced concrete structures based on the analysis of the electromagnetic signature of the steel rebar corrosion. The signature of the corrosion on the scattered field upon microwave illumination is first numerically analyzed. First-order quality factor parameters, the energy and the mean propagation delay, are proposed to quantify the corrosion amount in the structure. To validate the model, low-profile ultra-wide-band antennas (3-12 GHz) are fabricated and measured. Measurements on 12 reinforced concrete samples with induced corrosion are performed, using three different antenna setups. The experiments demonstrate a good correlation between an increase in the corrosion amount with a decrease in the energy and an increase in the time delay of the propagated signal.

A PatchMatch-Based Dense-Field Algorithm for Video Copy–Move Detection and Localization <=SEP=> We propose a new algorithm for the reliable detection and localization of video copy–move forgeries. Discovering well-crafted video copy–moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy–moves, we use a dense-field approach, with invariant features that guarantee robustness to several postprocessing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy–moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy–moves with good accuracy even in adverse conditions.

The Zachman Framework and the OMG''s Model Driven Architecture <=SEP=> 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 123456789012345678901234567890121234567890123456 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 1234567890123456789012345678901212345678901234567890123456789012123456789012345678901234567890121234 BUSINESS PROCESS TRENDS WHITEPAPER

Video summarisation: A conceptual framework and survey of the state of the art <=SEP=> Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users. 2007 Elsevier Inc. All rights reserved.