18,840 research outputs found
Circulant temporal encoding for video retrieval and temporal alignment
We address the problem of specific video event retrieval. Given a query video
of a specific event, e.g., a concert of Madonna, the goal is to retrieve other
videos of the same event that temporally overlap with the query. Our approach
encodes the frame descriptors of a video to jointly represent their appearance
and temporal order. It exploits the properties of circulant matrices to
efficiently compare the videos in the frequency domain. This offers a
significant gain in complexity and accurately localizes the matching parts of
videos. The descriptors can be compressed in the frequency domain with a
product quantizer adapted to complex numbers. In this case, video retrieval is
performed without decompressing the descriptors. We also consider the temporal
alignment of a set of videos. We exploit the matching confidence and an
estimate of the temporal offset computed for all pairs of videos by our
retrieval approach. Our robust algorithm aligns the videos on a global timeline
by maximizing the set of temporally consistent matches. The global temporal
alignment enables synchronous playback of the videos of a given scene
myTea: Connecting the Web to Digital Science on the Desktop
Bioinformaticians regularly access the hundreds of databases and tools that are available to them on the Web. None of these tools communicate with each other, causing the scientist to copy results manually from a Web site into a spreadsheet or word processor. myGrids' Taverna has made it possible to create templates (workflows) that automatically run searches using these databases and tools, cutting down what previously took days of work into hours, and enabling the automated capture of experimental details. What is still missing in the capture process, however, is the details of work done on that material once it moves from the Web to the desktop: if a scientist runs a process on some data, there is nothing to record why that action was taken; it is likewise not easy to publish a record of this process back to the community on the Web. In this paper, we present a novel interaction framework, built on Semantic Web technologies, and grounded in usability design practice, in particular the Making Tea method. Through this work, we introduce a new model of practice designed specifically to (1) support the scientists' interactions with data from the Web to the desktop, (2) provide automatic annotation of process to capture what has previously been lost and (3) associate provenance services automatically with that data in order to enable meaningful interrogation of the process and controlled sharing of the results
MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum
In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application
Information Compression, Intelligence, Computing, and Mathematics
This paper presents evidence for the idea that much of artificial
intelligence, human perception and cognition, mainstream computing, and
mathematics, may be understood as compression of information via the matching
and unification of patterns. This is the basis for the "SP theory of
intelligence", outlined in the paper and fully described elsewhere. Relevant
evidence may be seen: in empirical support for the SP theory; in some
advantages of information compression (IC) in terms of biology and engineering;
in our use of shorthands and ordinary words in language; in how we merge
successive views of any one thing; in visual recognition; in binocular vision;
in visual adaptation; in how we learn lexical and grammatical structures in
language; and in perceptual constancies. IC via the matching and unification of
patterns may be seen in both computing and mathematics: in IC via equations; in
the matching and unification of names; in the reduction or removal of
redundancy from unary numbers; in the workings of Post's Canonical System and
the transition function in the Universal Turing Machine; in the way computers
retrieve information from memory; in systems like Prolog; and in the
query-by-example technique for information retrieval. The chunking-with-codes
technique for IC may be seen in the use of named functions to avoid repetition
of computer code. The schema-plus-correction technique may be seen in functions
with parameters and in the use of classes in object-oriented programming. And
the run-length coding technique may be seen in multiplication, in division, and
in several other devices in mathematics and computing. The SP theory resolves
the apparent paradox of "decompression by compression". And computing and
cognition as IC is compatible with the uses of redundancy in such things as
backup copies to safeguard data and understanding speech in a noisy
environment
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