science

Large Scale Concept Ontology for Multimedia

Large Scale Concept Ontology for Multimedia is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Large Scale Concept Ontology for Multimedia rather than just read about it. In short: The Large-Scale Concept Ontology for Multimedia project was a series of workshops held from April 2004 to September 2006 for the purpose of defining a standard formal vocabulary for the annotation and retrieval of video. Mandate The Large-Scale Concept Ontology for Multimedia project was sponsored by the Disruptive Technology Office and brought together representatives from a variety of research communities, such as…

Key takeaways

Large Scale Concept Ontology for Multimedia belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Large Scale Concept Ontology for Multimedia to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Large Scale Concept Ontology for Multimedia from memory before moving on to harder problems.

Reference excerpt

The Large-Scale Concept Ontology for Multimedia project was a series of workshops held from April 2004 to September 2006 for the purpose of defining a standard formal vocabulary for the annotation and retrieval of video.

Mandate The Large-Scale Concept Ontology for Multimedia project was sponsored by the Disruptive Technology Office and brought together representatives from a variety of research communities, such as multimedia learning, information retrieval, computational linguistics, library science, and knowledge representation, as well as "user" communities such as intelligence agencies and broadcasters, to work collaboratively towards defining a set of 1,000 concepts. Individually, each concept was to meet the following criteria:

Utility: the concepts must support realistic video retrieval problems Feasibility: the concepts are capable or will be capable of detection given the near-term (5 year projected) state of technology Observibility: the concepts occur with relatively high frequency in actual video data sets Jointly, these concepts were to meet the additional criterion of providing broad (domain independent) coverage. High-level target areas for coverage included physical objects, including animate objects (such as people, mobs, and animals), and inanimate objects, ranging from large-scale (such as buildings and highways) to small-scale (such as telephones and appliances); actions and events; locations and settings; and graphics. The effort was led by Dr. Milind Naphade, who was the principal investigator along with researchers from Carnegie Mellon University, Columbia University, and IBM.

Development tracks The project had two main "tracks": the development and deployment of keyframe annotation tools (performed by CMU and Columbia), and the development of the Large-Scale Concept Ontology for Multimedia concept hierarchy itself. The second track was executed in two phases: The first consisted in the manual construction of an 884 concept hierarchy, was performed collaboratively among the research and user community representatives. The second track, performed by knowledge representation experts at Cycorp, Inc., involved the mapping of the concepts into the Cyc knowledge base and the use of the Cyc inference engine to semi-automatically refine, correct, and expand the concept hierarchy. The mapping/expansion phase of the project was motivated by a desire to increase breadth—the mapping had the effect of moving from 884 concepts to well past the initial goal of 1000—and to move Large-Scale Concept Ontology for Multimedia from a one-dimensional hierarchy of concepts, to a full-blown ontology of rich semantic connections.

Project results The outputs of the effort included:

A "lite" version of the Large-Scale Concept Ontology for Multimedia concept hierarchy consisting of a subset of 449 concepts. A corpus of 61,901 video keyframes, taken from the 2006 TRECVID data set, annotated using Large-Scale Concept Ontology for Multimedia "lite." The full taxonomy of 2,638 concepts, built semi-automatically by mapping 884 concepts, manually identified by collaborators, into the Cyc knowledge base, and querying the Cyc inference engine for useful additions. The full ontology, in the form of a 2006 ResearchCyc release that contained the Large-Scale Concept Ontology for Multimedia mappings into the Cyc ontology.

Public detectors Several sets of concept detectors were developed and released for public use:

VIREO-374, 374 detectors developed by City University of Hong Kong. Columbia374, 374 detectors developed by Columbia University. Mediamill101, 101 detectors developed by The University of Amsterdam.

Use in the larger research community Since its release, Large-Scale Concept Ontology for Multimedia has begun to be used successfully in visual recognition research: Apart from research done by project participants, it has been used by independent research in concept extraction from images, and has served as the basis for a video annotation tool.

See also Multimedia Web Ontology Language (MOWL)

References

External links Large-Scale Concept Ontology for Multimedia homepage

Worked examples

Example 1 — a first encounter with Large Scale Concept Ontology for Multimedia

Start with the simplest possible case. Write down what Large Scale Concept Ontology for Multimedia claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Large Scale Concept Ontology for Multimedia before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Large Scale Concept Ontology for Multimedia ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Large Scale Concept Ontology for Multimedia

In research: Large Scale Concept Ontology for Multimedia appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Large Scale Concept Ontology for Multimedia in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Large Scale Concept Ontology for Multimedia is common in secondary-school and first-year university syllabi. It links to neighbouring topics Multimedia, so understanding it makes those chapters shorter.
In everyday life: Look for Large Scale Concept Ontology for Multimedia outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Large Scale Concept Ontology for Multimedia in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Large Scale Concept Ontology for Multimedia means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Large Scale Concept Ontology for Multimedia out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Large Scale Concept Ontology for Multimedia in simple terms?

Why does Large Scale Concept Ontology for Multimedia matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Large Scale Concept Ontology for Multimedia?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Large Scale Concept Ontology for Multimedia.