Investigating visualisation techniques for rapid triage of digital forensic evidence

This study investigates the feasibility of a tool that allows digital forensics (DF) investigators to efficiently triage device datasets during the collection phase of an investigation. This tool utilises data visualisation techniques to display images found in near real-time to the end user. Findings indicate that participants were able to accurately identify contraband material whilst using this tool, however, classification accuracy dropped slightly with larger datasets. Combined with participant feedback, the results show that the proposed triage method is indeed feasible, and this tool provides a solid foundation for the continuation of further work

Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.Comment: BMVC 201

Consolidation of complex events via reinstatement in posterior cingulate cortex

It is well-established that active rehearsal increases the efficacy of memory consolidation. It is also known that complex events are interpreted with reference to prior knowledge. However, comparatively little attention has been given to the neural underpinnings of these effects. In healthy adult humans, we investigated the impact of effortful, active rehearsal on memory for events by showing people several short video clips and then asking them to recall these clips, either aloud (Experiment 1) or silently while in an MRI scanner (Experiment 2). In both experiments, actively rehearsed clips were remembered in far greater detail than unrehearsed clips when tested a week later. In Experiment 1, highly similar descriptions of events were produced across retrieval trials, suggesting a degree of semanticization of the memories had taken place. In Experiment 2, spatial patterns of BOLD signal in medial temporal and posterior midline regions were correlated when encoding and rehearsing the same video. Moreover, the strength of this correlation in the posterior cingulate predicted the amount of information subsequently recalled. This is likely to reflect a strengthening of the representation of the video's content. We argue that these representations combine both new episodic information and stored semantic knowledge (or "schemas"). We therefore suggest that posterior midline structures aid consolidation by reinstating and strengthening the associations between episodic details and more generic schematic information. This leads to the creation of coherent memory representations of lifelike, complex events that are resistant to forgetting, but somewhat inflexible and semantic-like in nature

DramaQA: Character-Centered Video Story Understanding with Hierarchical QA

Despite recent progress on computer vision and natural language processing, developing video understanding intelligence is still hard to achieve due to the intrinsic difficulty of story in video. Moreover, there is not a theoretical metric for evaluating the degree of video understanding. In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story. The DramaQA focused on two perspectives: 1) hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) character-centered video annotations to model local coherence of the story. Our dataset is built upon the TV drama "Another Miss Oh" and it contains 16,191 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels. We provide 217,308 annotated images with rich character-centered annotations, including visual bounding boxes, behaviors, and emotions of main characters, and coreference resolved scripts. Additionally, we provide analyses of the dataset as well as Dual Matching Multistream model which effectively learns character-centered representations of video to answer questions about the video. We are planning to release our dataset and model publicly for research purposes and expect that our work will provide a new perspective on video story understanding research.Comment: 21 pages, 10 figures, submitted to ECCV 202

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows