Video Context Research Articles

Effects of similarity on environmental context cueing

Three experiments examined the prediction that context cues which are similar to study contexts can facilitate episodic recall, even if those cues are never seen before the recall test. Environmental context cueing effects have typically produced such small effect sizes that influences of moderating factors, such as the similarity between encoding and retrieval contexts, would be difficult to observe experimentally. Videos of environmental contexts, however, can be used to produce powerful context-dependent memory effects, particularly when only one memory target is associated with each video context, intentional item-context encoding is encouraged, and free recall tests are used. Experiment 1 showed that a not previously viewed video of the study context provided an effective recall cue, although it was not as effective as the originally viewed video context. Experiments 2 and 3 showed that videos of environments that were conceptually similar to encoding contexts (e.g., both were videos of ball field games) also cued recall, but not as well if the encoding contexts were given specific labels (e.g., “home run”) incompatible with test contexts (e.g., a soccer scene). A fourth experiment that used incidental item-context encoding showed that video context reinstatement has a robust effect on paired associate memory, indicating that the video context reinstatement effect does not depend on interactive item-context encoding or free recall testing.

Advertising object in web videos

We have witnessed the booming of contextual video advertising in recent years. However, those advertisement systems solely take the metadata into account, such as titles, descriptions and tags. This kind of text-based contextual advertising reveals a number of shortcomings in ads insertion and ads association. In this paper, we present a novel video advertising system called VideoAder. The system leverages the well organized media information from the video corpus for embedding visual content relevant ads into a set of precisely located insertion position. Given a product, we utilize content-based object retrieval technique to identify the relevant ads and their potential embedding positions in the video stream. Then we formulate the ads association as an optimization problem to maximize the total revenue for the system. Specifically, the “Single-Merge” and “Merge” methods are proposed to tackle the complex query in visual representation. Typical Feature Intensity (TFI) is used to train a classifier to automatically decide which method is more representive. Experimental results demonstrated the accuracy and feasibility of the system.

Supramodal neural processing of abstract information conveyed by speech and gesture

ness and modality of interpersonal communication have a considerable impact on comprehension. They are relevant for determining thoughts and constituting internal models of the environment. Whereas concrete object-related information can be represented in mind irrespective of language, abstract concepts require a representation in speech. Consequently, modality-independent processing of abstract information can be expected. Here we investigated the neural correlates of abstractness (abstract vs. concrete) and modality (speech vs. gestures), to identify an abstractness-specific supramodal neural network. During fMRI data acquisition 20 participants were presented with videos of an actor either speaking sentences with an abstract-social [AS] or concrete-object-related content [CS], or performing meaningful abstract-social emblematic [AG] or concrete-object-related tool-use gestures [CG]. Gestures were accompanied by a foreign language to increase the comparability between conditions and to frame the communication context of the gesture videos. Participants performed a content judgment task referring to the person vs. object-relatedness of the utterances. The behavioral data suggest a comparable comprehension of contents communicated by speech or gesture. Furthermore, we found common neural processing for abstract information independent of modality (AS > CS ∩ AG > CG) in a left hemispheric network including the left inferior frontal gyrus (IFG), temporal pole, and medial frontal cortex. Modality specific activations were found in bilateral occipital, parietal, and temporal as well as right inferior frontal brain regions for gesture (G > S) and in left anterior temporal regions and the left angular gyrus for the processing of speech semantics (S > G). These data support the idea that abstract concepts are represented in a supramodal manner. Consequently, gestures referring to abstract concepts are processed in a predominantly left hemispheric language related neural network.

Exploratory Visualization of Surgical Training Databases for Improving Skill Acquisition

A new visualization system analyzes multidimensional surgical performance databases of information collected via emerging surgical robot and simulator technologies. In particular, it has visualized force, position, rotation, and synchronized video data from 300 bimanual laparoscopic surgery tasks performed by more than 50 surgeons. To explore data, the system uses a multiple-coordinated-views framework. It provides techniques to select and filter multivariate time series data, visualize animated force plots in conjunction with contextual videos, encode multivariate bimanual tool trace data in 3D visualizations, and link visualizations to a database management system via a new generalizable data model. Insights and feedback from an interdisciplinary iterative design process and use case studies support the utility of visualization in this emerging area of data-driven surgical training.

Changes in task-extrinsic context do not affect the persistence of long-term cumulative structural priming

We present two experiments exploring the role of extrinsic memory factors (i.e., factors that are extrinsic to the primary task that is being performed) and intrinsic memory factors (i.e., factors that are intrinsic to the primary task being completed) in the persistence of cumulative structural priming effects. Participants completed a two-phase experiment, where the first phase established a bias toward producing either the double object or prepositional object construction, and the second phase assessed the effects of this bias. Extrinsic memory factors were manipulated by having participants complete the two phases of the study in the same or different locations (physical context change) or while watching the same or different videos (video context change). Participants completed the second phase of the study 10min after the first phase of the study in Experiment 1, and after a delay of 1week in Experiment 2. Results suggest that the observed structural priming effects were not affected by manipulations of extrinsic memory factors. These data suggest that explicit memory does not play a large role in the long-term persistence of cumulative structural priming effects.

Brief Report: Impaired Differentiation of Vegetative/Affective and Intentional Nonverbal Vocalizations in a Subject with Asperger Syndrome (AS)

The Asperger syndrome (AS) includes impaired recognition of other people's mental states. Since language-based diagnostic procedures may be confounded by cognitive-linguistic compensation strategies, nonverbal test materials were created, including human affective and vegetative sounds. Depending on video context, each sound could be interpreted either as direct expression of an agent's affective/vegetative state or as result of intentional-executive mental operations. "Situational relevance" and "intentionality" ratings by a group of twelve healthy subjects nicely differentiated between context types. By contrast, an AS subject showed a systematic over-interpretation of vegetative/affective signals in terms of planned activities. Such overestimation of intentional motivation, leading to impaired social cognition, might be due to the inability to utilize "affective resonance" mechanisms for the interpretation of an individual's internal state.

Translation and Capture Architecture for Output of Conversational Utterances

Architecture that combines capture and translation of concepts, goals, needs, locations, objects, locations, and items (e.g., sign text) into complete conversational utterances that take a translation of the item, and morph it with fluidity into sets of sentences that can be echoed to a user, and that the user can select to communicate speech (or textual utterances). A plurality of modalities that process images, audio, video, searches and cultural context, for example, which are representative of at least context and/or content, and can be employed to glean additional information regarding a communications exchange to facilitate more accurate and efficient translation. Gesture recognition can be utilized to enhance input recognition, urgency, and/or emotional interaction, for example. Speech can be used for document annotation. Moreover, translation (e.g., speech to speech, text to speech, speech to text, handwriting to speech, text or audio, . . . ) can be significantly improved in combination with this architecture.

Robust video super resolution algorithm using measurement validation method and scene change detection

Abstract Explicit motion estimation is considered a major factor in the performance of classical motion-based super resolution (SR) algorithms. To reconstruct video frames sequentially, we applied a dynamic SR algorithm based on the Kalman recursive estimator. Our approach includes a novel measurement validation process to attain robust image reconstruction results under inexplicit motion estimation. In our method, the suitability for high-resolution pixel estimation is determined by the accuracy of motion estimation. We measured the accuracy of the image registration result using the Mahalanobis distance between the input low-resolution frame and the motion compensated high-resolution estimation. We also incorporate an effective scene change detection method dedicated to the proposed SR approach for minimizing erroneous results when abrupt scene changes occur in the video frames. According to the ratio of well-aligned pixels (i.e., motion is compensated accurately) to the total number of pixels, we are able to detect sudden changes of scene and context in the input video. Representative experiments on synthetic and real video data show robust performance of the proposed algorithm in terms of its reconstruction quality even with errors in the estimated motion.

Ravishing Beauty

Research Article| July 01 2011 Ravishing Beauty: The Aesthetics of Rape in Grace Ndiritu’s Video Desert Storm Caroline Bagenal Caroline Bagenal CAROLINE BAGENAL is an artist and writer. She is an associate professor at Montserrat College of Art in Beverly, Massachusetts. Her website is www.carolinebagenal.com. Search for other works by this author on: This Site PubMed Google Scholar Afterimage (2011) 39 (1-2): 63–65. https://doi.org/10.1525/aft.2011.39.1-2.63 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Caroline Bagenal; Ravishing Beauty: The Aesthetics of Rape in Grace Ndiritu’s Video Desert Storm. Afterimage 1 July 2011; 39 (1-2): 63–65. doi: https://doi.org/10.1525/aft.2011.39.1-2.63 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAfterimage Search This content is only available via PDF. © 2011 Afterimage/Visual Studies Workshop, unless otherwise noted. Reprints require written permission and acknowledgement of previous publication in Afterimage.2011 Article PDF first page preview Close Modal You do not currently have access to this content.

Spatial video and GIS

GIS elemental unit representations of spatial data are often defined in terms of points, lines and areas. However, another type of spatial data that is becoming frequently captured, but as yet is largely ignored in GIS, is that of video. While digital video recording is a commonly encountered medium in modern society and encompasses many forms, from simple personal camcorders through to sophisticated survey and surveillance systems, its geographical representation in a GIS has not been fully examined or realised. In the majority of cases the video footage is usually captured while the device and/or the objects being viewed are in motion. What is of particular interest is when video streams can be, or have been, associated with spatial data such as location and orientation to create geographically referenced videographic data, which, for simplicity, will be defined as spatial video. Fundamentally, the nature of video is to record space, so when spatial properties can be accurately acquired and associated with this footage, an important geographical element can be considered for integration and analysis within a GIS. Existing spatial video systems, both commercial and research, are predominantly used in survey or LBS roles and are usually bespoke and application specific. These systems do not model spatial video to any recognised standards that is generalised to be both data and platform independent. They do not support GIS integration and/or analysis from a purely spatial content perspective. A video-image/remote-sensing centric approach prevails where usage options range from simple visualisation interfaces to interactive computer vision systems. What has been largely overlooked is a spatial approach where the inherent geographical extent recorded in each video frame can be modelled and used in a geo-spatial analysis context. While this modelling approach has not been fully realised, it does exist in a GIS form based on Open Geospatial Consortium standards, where the spatial context of video is defined in a structure called a ViewCone. However, a ViewCone only defines a 2D model of the geographical extent of each frame and is restricted to a three-or-five sided polygon representation. Thus, this article examines the potential of modelling spatial video through the use of elemental data types within GIS; gives some examples of using this approach; describes some problems in using spatial video within GIS; and then demonstrates how these problems are being solved. This is done in three stages: Firstly, a detailed overview of spatial video in its current GIS role is provided – this is achieved through a complete introduction to the distinct elements of spatial video followed by a review of its use in both commercial and academic application areas. Secondly, a brief theoretical overview of an alternative GIS-constrained ViewCone data structure is given that defines a more flexible spatial video model for both 2D and 3D GIS analysis and visualisation. Thirdly, a selective sample of results is presented based on an implementation of this approach being applied to a constrained spatial video data source in a specific study area.

Contextual Video Recommendation by Multimodal Relevance and User Feedback

With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.

Barriers and Benefits Associated with Nurses Information Seeking Related to Patient Education Needs on Clinical Nursing Units

The purpose of this study was to answer the following two questions: What are clinical nurses’ rationales for their approaches to finding patient educational materials on the web? What are perceived barriers and benefits associated with the use of web-based information resources for patient education in the context of nursing clinical practice?Over 179 individual data units were analyzed to understand clinical nurses’ rationales for their approaches to find patient educational materials on the web. Rationales were defined as those underlying catalysts or activators leading to an information need. Analyses found that the primary reasons why clinical nurses conducted web-based information searches included direct patient requests ( 9 requests), colleague requests (6 requests), building patient materials collections (4), patients’ family requests (3), routine teaching (1), personal development (1), or staff development (1). From these data, four broad themes emerged: professional reasons, personal reasons, technology reasons, and organization reasons for selecting information resources. Content analysis identified 306 individual data units representing either ‘benefits’ (178 units) or ‘barriers’ (128) to the nurses’ use of web resources for on-unit patient care. Inter-rater reliability was assessed and found to be excellent (r = 0.943 to 0.961). The primary themes that emerged as barriers to the used of web-based resources included: 1) time requirements to perform a search, 2) nurses’ experience and knowledge about the resources or required technology, 3) specific characteristics of individuals electronic information resources, and 4) organizational procedures and policies. Three primary themes that represented the benefits of using web-based resources were also identified: 1) past experiences and knowledge of a specific resource or the required technologies, 2) availability and accessibility on the unit, and 3) specific characteristics of individual information tool. In many cases, nurses commented on specific characteristics or features of favorite information resources. Favorite sites included a variety or reputable health care organizations that displayed context in text, audio, and/or video. In addition such sites were described as easy-to read and provided content related to patient-focused information or specific content such as toll free telephone contact numbers.Information searching is the interaction between and among information users and computer-based information systems. Information seeking is becoming an important part of the knowledge work of nurses. Information seeking and searching intersects with the field of human computer interaction (HCI), which focuses on all aspects of human, and computer interactions. Users of an information system are understood as “actors” in situations, with a set of skills and shared practices based on work experiences with others. Designing better tools and developing information searching strategies that support, extend, and transform practices, begins by asking: Who are the users? What are the tasks? What is the interplay between the technology and the organization of the task? This study contributes fundamental data and information about the rationales nurses use in information seeking tasks. In addition it provides empirical evidences regarding barriers and benefits of information seeking in the context of patient education needs in inpatient clinical settings.

Representing context in video records of practice for urban mathematics teacher education

Mathematics education research has not sufficiently theorized about mathematics teacher knowledge and practice, teacher learning, and teacher education in ways that are reflective of the specificities of the sociopolitical contexts of schooling. In the USA, this is particularly important for urban mathematics education. This paper examines the affordances and challenges of representing context in video records of practice, particularly in the urban context, for use in the preparation of mathematics teachers for urban settings. The discussion, grounded in current research and theory relevant to representations of teaching, urban education, and mathematics teacher education, takes up three key issues: how is a focus on the urban context relevant to the design of video records of practice for mathematics teacher education? How can video records support prospective teachers’ understandings of the sociopolitical contexts of mathematics teaching? How does a focus on the urban context impact the meaning teachers make of video records?

Adaptive Subspace Symbolization for Content-Based Video Detection

Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.

AdOn: toward contextual overlay in-video advertising

This paper presents a contextual video advertising system, called AdOn, which supports intelligent overlay in-video advertising. Unlike most current ad-networks such as Youtube that overlay the ads at fixed locations in the videos (e.g., on the bottom fifth of videos 15 s in), AdOn is able to automatically detect a set of spatio-temporal non-intrusive locations and associate the contextually relevant ads with these locations. The overlay ad locations are obtained on the basis of video structuring, face and text detection, as well as visual saliency analysis, so that the intrusiveness to the users can be minimized. The ads are selected according to content-based multimodal relevance so that the relevance can be maximized. AdOn represents one of the first attempts towards contextual overlay video advertising by leveraging information retrieval and multimedia content analysis techniques. The experiments conducted on a video database with more than 100 video programs and 7,000 ad products indicated that AdOn is superior to existing advertising approaches in terms of ad relevance and user experience.

Video context-dependent recall

In two experiments, we used an effective new method for experimentally manipulating local and global contexts to examine context-dependent recall. The method included video-recorded scenes of real environments, with target words superimposed over the scenes. In Experiment 1, we used a within-subjects manipulation of video contexts and compared the effects of reinstatement of a global context (15 words per context) with effects of less overloaded context cues (1 and 3 words per context) on recall. The size of the reinstatement effects in Experiment 1 show how potently video contexts can cue recall. A strong effect of cue overload was also found; reinstatement effects were smaller, but still quite robust, in the 15 words per context condition. The powerful reinstatement effect was replicated for local contexts in Experiment 2, which included a no-contexts-reinstated group, a control condition used to determine whether reinstatement of half of the cues caused biased output interference for uncued targets. The video context method is a potent way to investigate context-dependent memory.

A terrestrial animal-borne video system for large mammals

Animal-borne video and environmental data collection systems (AVEDs) are integrated sensor systems that collect video from the animal's perspective and combine it with data from other sensors, including audio, location, temperature, acceleration, and other data. By placing sensor data within the context of video, AVEDs provide a unique perspective not offered by other methods and facilitate research into animal behavior and foraging tactics, animal energetics, wildlife damage issues, and inter- and intra-specific interactions. Marine AVEDs have outpaced their terrestrial counterparts in technological sophistication and store all data onboard. Terrestrial systems have been transmission-based and thus have been deployed on species that are easily tracked or habituated to humans. We present the first terrestrial, store-onboard AVED developed for large mammals and demonstrate the utility of our AVEDs by quantifying and describing contacts among white-tailed deer ( Odocoileus virginianus). Our system was largely developed from commercially available equipment and included a camera, microcomputer, and a sensor board attached to a neck collar with affixed battery packs. We deployed the system on 2 semi-wild and 2 free-ranging deer. We collected 12.2, 12.3, 30.3, and 41.6 h of video during our deployments; the 2 lowest recording durations were the result of blown fuses in the battery packs. We observed 11 contacts among white-tailed deer during 4 deployments. Contacts comprised a total of 22.8 min ( x ¯ = 2.1 min / contact , SE = 0.7). Our AVEDs revealed several behaviors during contacts between individual white-tailed deer, including inspection, avoidance, and grooming. These data are not captured by other techniques (e.g., radio telemetry) and demonstrate the utility of continuous video and other sensors from the animal's perspective. Although AVEDs are a powerful new tool for wildlife research and management, their use should be preceded by careful formulation of research and management objectives.

Combining scene model and fusion for night video enhancement

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene information and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.

Swarm Intelligence Based Fuzzy C-Means Clustering for Motion Vector Selection in Video Watermarking

This work develops a novel video watermarking system in which motion vectors define the locations of watermarks, and the number of watermark bits vary dynamically among frames. Swarm intelligence based Fuzzy C-means (SI-FCM) clustering method is employed to determine the motion vectors and the positions of the watermarks. The proposed scheme has two merits. First, the watermarking strategy does not involve manually setting the location of the watermark bits. Second, the proposed special exclusive-OR operation closely relates the watermark bit to the context of video, preventing attackers from discovering the real watermark length of each frame. Therefore, the proposed approach is highly secure. Video watermarking is achieved using swarm intelligence based FCM clustering which provides a different view from the conventional thresholding method of determining watermark embedding for motion vectors. Experimental results reveal that the watermarked video retains satisfactory quality with very low degradation.

Towards efficient context-specific video coding based on gaze-tracking analysis

This article discusses a framework for model-based, context-dependent video coding based on exploitation of characteristics of the human visual system. The system utilizes variable-quality coding based on priority maps which are created using mostly context-dependent rules. The technique is demonstrated through two case studies of specific video context, namely open signed content and football sequences. Eye-tracking analysis is employed for identifying the characteristics of each context, which are subsequently exploited for coding purposes, either directly or through a gaze prediction model. The framework is shown to achieve a considerable improvement in coding efficiency.