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by: Adaline Pollich


Adaline Pollich
GPA 3.81

Ching Cheung

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About this Document

Ching Cheung
Class Notes
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Popular in Electrical Engineering

This 15 page Class Notes was uploaded by Adaline Pollich on Friday October 23, 2015. The Class Notes belongs to EE 639 at University of Kentucky taught by Ching Cheung in Fall. Since its upload, it has received 16 views. For similar materials see /class/228317/ee-639-university-of-kentucky in Electrical Engineering at University of Kentucky.




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Date Created: 10/23/15
Multimedia Information Systems Samson Cheung EE 639 Fall 2004 Lecture 2 Course Overview igital Video Librarx MetaData Segmentation Relevance Feedback Video Archive Compression Content Protection S ummarization Content Protection I Problems I Forgery detection I Authentication I Tracking I Secure distribution scrambling I Cover I Basic DRM concept and infrastructure I Digital Watermarking Watermarking and Content Protection RX Verify the watermar39 D Stenography vs watermarking Watermark I Visible vs invisible I Spatial vs frequency domain I Fragile vs robust vs semifragile Applications copyright user ID content ID authentication data hiding Compression amp Deliver I Problems I Large Data Size Digital video 720x480x24x30 249 Mbps 112 GB per hour I Variation in capabilities in receivers cell phone PDA PC TV HDTV I Variation in network mobile WiFi Internet Satellite I Cover I Review image video audio and graphics compression I Survey scalable compression joint sourcechannel coding multiple description distributed compression Segmentation I Problem I Break down complex audiovisual objects into atomic units for retrieval I Cover I Basic image segmentation I Video segmentation shot and story level I Special events detection 0 Face 0 Object Tracking 0 Video Structure Modeling Feature Extraction I Problems I Represent an audiovisual object as a multiple vector with a multiple distance function d for measuring similarity I Example I Vector Color histogram I Distance L1 KL Divergence Earthmover I Cover I Simple features for image video audio 3D objects I Various similarity functions Similarity Search amp Indexing I Similarity Search Given a query vector q find v in a database D of feature vectors such that dqv is the smallest most similar I Problem I D could be very large I Sequential is slow Logarithmic search is very desirable I The dimension of v is very high or even uncountable eg graph of spatial layout I Curse of dimensionality in highdimensional space most fast search methods become sequential I Cover feature selection dimension reduction clusteringbased data structure trees and randomized search Relevance Feedback I Problems I Simple distance function does not reflect human s notion of similarity gt Learn from human I Cover I Classification Define three different classes Ppositive Nnegative Ddon t care with respect to a query q Given a small human labeled set for each class identify all v in a database D that belongs to class P I Learning Minimize the size of labeled sets Summarization I Problem I How to present 100 clips of video each 3 hours long to a user to select I Limited capability of receivers I Cover I Keyframes Mosaicing I Hierarchical Clustering I Spatial Summary and visualization I Skim Examples of Summarization a Level 1 Clustering ul39ull IJxe iden shots by lempmul m ianccs r 7 V a N J c Anclmrpcrwn shuts under he Ih39d class at level 2 Structural Parsing Shot removal Skim Drastically condensed audioVideo clips MetaData I Problem I Auxiliary information I Unified textual description of features and semantic concepts I Highly searchable I Cover I XML MPEG7 MPEG21 I Traditional IR techniques on texts and hypertext ltEvent id Goal gt ltLabelgt ltTerm CSLocationquothttpIlwwwCSscomSportsquot CSTermldquot43quotgt ltLabe xmlangquotenusquotgt Goal ltILabelgt ltITermgt ltILabelgt ltSemanticLocationgt ltPacegt ltPaceName xmlang en gt Santiago Bernabeu ltIPaceNamegt ltCountrygt Spain ltICountrygt ltPostaAddressgt ClFabregas No 26 Barcelona ltIPostaAddressgt ltIPacegt ltISemanticLocationgt ltSemanticTimegt ltDe nitiongt ltFreeTextgt 833pm Saturday March 20 2000 ltIFreeTextgt ltIDe nitiongt ltISemanticTimegt ltMedia0ccurencegt ltMediaLocatorgtltMediaTimegt ltMediaTimePointgt T000 ltIMediaTimePointgt ltMediaDurationgt PT5S ltIMediaDurationgt ltIMediaTimegtltlMediaLocatorgt ltIMediaOccurencegt ltIEventgt Content Semantic DescriBtion ltSemantic idquotS1quotgt ltObject idquotForwarclobquotgt ltlObjectgt ltObject idquotBal obquotgt ltlObjectgt ltObject idquotGoalKeeperobquotgt ltlObjectgt ltObject idquotGoalobquotgt ltlObjectgt ltEvent idquotGoalevquotgt ltEvent idquotKickevquotgt ltlEventgt ltEvent idquotNotCatchevquotgt ltlEventgt ltEvent idquotEnterevquotgt ltlEventgt ltlEventgt ltSemanticGraphgt ltEclge namequotagentquot sourcequotKickevquot targetquotForwardobquotlgt ltEclge namequotpatientquot sourcequotKickevquot targetquotBal obquotlgt ltEclge namequotdestinationquot sourcequotKickevquot targetquotGoalobquotlgt ltEclge namequotagentquot sourcequotEnterevquot targetquotBallobquotlgt ltEclge namequotpatientquot sourcequotEnterevquot targetquotGoalobquotlgt ltSemanticGraphgt ltlSemanticgt Course format I 3 lectures per week I 6 homeworks I 1 final project I Two types of projects 0 Experimental or Implementation up to three persons 0 Survey single person I midterm proposal I final report and presentation I may be extended to Advanced Projects


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