Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic

TV-uutisten automaattinen jäsentäminen : segmentointi ja jaksojen tunnistaminen digitaalisen videon automaattisen indeksoinnin perustana

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Kernel-Based Methods for Hypothesis Testing: A Unified View

International audienceKernel-based methods provide a rich and elegant framework for developing nonparametric detection procedures for signal processing. Several recently proposed procedures can be simply described using basic concepts of reproducing kernel Hilbert space embeddings of probability distributions, namely mean elements and covariance operators. We propose a unified view of these tools, and draw relationships with information divergences between distributions

User needs in television archive access:Acquiring knowledge necessary for system design

This paper presents a methodical approach for generating deep knowledge about users, as a prerequisite for design and construction of digital information access to cultural heritage information objects. We exemplify this methodical approach by reporting on an explorative study of information need characteristics in a television broadcast context. The methodical approach is inspired by naturalistic research, and our main data is nine in-depth interviews conducted with scholars and students within the academic field of Media Studies. The analysis identifies four characteristics. Firstly, broadcasts are needed as objects of analysis in empirical research. Secondly, the needs are related to three broadcast dimensions: 1) Transmission; 2) Archive; and 3) Reception. Thirdly, four fundamental types of information needs are verified in a television broadcast context: 1) Known item; 2) Factual data; 3) Known topic or content; and 4) Muddled topic or content. Fourthly, the interviewees’ needs consist of four phases: 1) Getting an overview of transmitted broadcasts; 2) Identification of borderline exemplars; 3) Selection of specific programmes; and 4) Verification of facts. The present paper presents novel research on characteristics of information needs in a television broadcast context. We demonstrate how one may go about generating knowledge which is imperative for the design and construction of future broadcast retrieval systems

Graphics Recognition -- from Re-engineering to Retrieval

Invited talk. Colloque avec actes et comité de lecture. internationale.International audienceIn this paper, we discuss how the focus in document analysis, generally speaking, and in graphics recognition more specifically, has moved from re-engineering problems to indexing and information retrieval. After a review of ongoing work on these topics, we propose some challenges for the years to come

Identification, indexing, and retrieval of cardio-pulmonary resuscitation (CPR) video scenes of simulated medical crisis.

Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. This process can be tedious and retrieval of specific video segments should be automated. In this dissertation, we propose a machine learning based approach to detect and classify scenes that involve rhythmic activities such as Cardio-Pulmonary Resuscitation (CPR) from training video sessions simulating medical crises. This applications requires different preprocessing techniques from other video applications. In particular, most processing steps require the integration of multiple features such as motion, color and spatial and temporal constrains. The first step of our approach consists of segmenting the video into shots. This is achieved by extracting color and motion information from each frame and identifying locations where consecutive frames have different features. We propose two different methods to identify shot boundaries. The first one is based on simple thresholding while the second one uses unsupervised learning techniques. The second step of our approach consists of selecting one key frame from each shot and segmenting it into homogeneous regions. Then few regions of interest are identified for further processing. These regions are selected based on the type of motion of their pixels and their likelihood to be skin-like regions. The regions of interest are tracked and a sequence of observations that encode their motion throughout the shot is extracted. The next step of our approach uses an HMM classiffier to discriminate between regions that involve CPR actions and other regions. We experiment with both continuous and discrete HMM. Finally, to improve the accuracy of our system, we also detect faces in each key frame, track them throughout the shot, and fuse their HMM confidence with the region\u27s confidence. To allow the user to view and analyze the video training session much more efficiently, we have also developed a graphical user interface (GUI) for CPR video scene retrieval and analysis with several desirable features. To validate our proposed approach to detect CPR scenes, we use one video simulation session recorded by the SPARC group to train the HMM classifiers and learn the system\u27s parameters. Then, we analyze the proposed system on other video recordings. We show that our approach can identify most CPR scenes with few false alarms