18,322 research outputs found
Exploring EEG for Object Detection and Retrieval
This paper explores the potential for using Brain Computer Interfaces (BCI)
as a relevance feedback mechanism in content-based image retrieval. We
investigate if it is possible to capture useful EEG signals to detect if
relevant objects are present in a dataset of realistic and complex images. We
perform several experiments using a rapid serial visual presentation (RSVP) of
images at different rates (5Hz and 10Hz) on 8 users with different degrees of
familiarization with BCI and the dataset. We then use the feedback from the BCI
and mouse-based interfaces to retrieve localized objects in a subset of TRECVid
images. We show that it is indeed possible to detect such objects in complex
images and, also, that users with previous knowledge on the dataset or
experience with the RSVP outperform others. When the users have limited time to
annotate the images (100 seconds in our experiments) both interfaces are
comparable in performance. Comparing our best users in a retrieval task, we
found that EEG-based relevance feedback outperforms mouse-based feedback. The
realistic and complex image dataset differentiates our work from previous
studies on EEG for image retrieval.Comment: This preprint is the full version of a short paper accepted in the
ACM International Conference on Multimedia Retrieval (ICMR) 2015 (Shanghai,
China
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
User centred evaluation of a recommendation based image browsing system
In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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
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