计算机专业英语专业知识

– Second generation (content-based): objective
➢Image contents → Image
Semantic gap
Байду номын сангаас
– Current way (semantic): subjective + objective ➢Image contents + semantic feature → Image
计算机专业英语专业知识
Abbreviations
• 3D—3 Dimension • ACM—Association for Computing Machinery • AI—Artificial Intelligence • ALT—Advanced Learning Technology • API—Application Programming Interface • CAD—Computer Aided Design • CBIR—Content-Based Image Retrieval • CBVIR—Content-Based Visual Information Retrieval • QBIC—Query by Image Content • CG—Computer Graphics
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Content-based Image Retrieval（CBIR)
Think About…
What is content-based image retrieval? What does the term “content” mean? What are low level image retrieval, region based image retrieval, and semantic image retrieval respectively? From the historic overview, how has CBIR evolved? What does it mean by multimedia information retrieval ?
– Our way: ➢Image contents + semantic feature + keywords/captions → Image
What Is Content-based Image Retrieval
• “Content-based” means that the search will analyze the actual contents of the image. The term “content” in this context might refer colors, shapes, textures, or any other information that can be derived from the image itself
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Major Tasks in NLP
• Natural language generation • Optical Character Recognition • Question answering • Speech recognition • Spoken dialogue system • Text simplification • Text to speech • Text-proofing
Natural Language Processing (NLP)
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Think About…
What research area does natural language processing belong to? What problems does it try to resolve? What do natural-language-generation systems and natural-language-understanding systems do respectively? What problems seem difficult in natural language processing? What are the major tasks in NLP?
Digital Image Retrieval
• Search for digital images in large databases
– First generation: laborious, subjective
➢Metadata (captions or keywords) →Image
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What is Natural Language Processing
• Natural-language-generation systems convert information from computer databases into normal-sounding human language. Natural-language-understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate.
Text Segmentation
• Some written languages like Chinese, Japanese and Thai do not have singleword boundaries either, so any significant text parsing usually requires the identification of word boundaries, which is often a non-trivial task.
• In most spoken languages, the sounds representing successive letters blend into each other, so the conversion of the analog signal to discrete characters can be a very difficult process.
Low Level Image Retrieval
• Color
– Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color searches will usually involve comparing color histograms, though this is not the only technique in practice
What Research Areas Are Involved In?
• Computer vision, pattern recognition, image processing, data mining, machine learning, human-computer interaction, artificial intelligence
Technology for Development • IEEE—Institute of Electrical and Electronics Engineers
Abbreviations
• ISO—International Organization for Standardization • LBS—Location-based Services • MD—Molecular Dynamics • MIS—Management Information System • NLP—Natural Language Processing • NMR—Nuclear Magnetic Resonance • PDF—Portable Document Format • Ph.D.—Doctor of Philosophy • RDF—Resource Description Framework • SDK—Software Development Kit
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Difficulties
• Speech Segmentation • Text Segmentation • Word Sense Disambiguation
– Many words have more than one meaning
• Syntactic Ambiguity
– The grammar for natural languages is ambiguous
Abbreviations
• TIFF—Tagged Image • URL—Uniform Resource Locator • VLE—Virtual Learning Environment • VRML—Virtual Reality Modeling Language • W3C—World Wide Web Consortium • XML—eXtensible Markup Language • ERP—Enterprise resource planning software • e.g.—exempli gratia • i.e.—id est
• Imperfect or Irregular Input
– Foreign or regional accents and vocal impediments in speech; typing or grammatical errors