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1 0. Organizational Issues Multimedia Databases Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig Lecture :00-17:15 (3 lecture hours with a break) Exercises, detours, and home work discussion integrated into lecture 4 Credits Exams Oral exam 50% of exercise points needed to be eligible for the exam Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 2 0. Organizational Issues Recommended literature Schmitt: Ähnlichkeitssuche in Multimedia-Datenbanken, Oldenbourg, 2005 Steinmetz: Multimedia-Technologie: Grundlagen, Komponenten und Systeme, Springer, Organizational Issues Castelli/Bergman: Image Databases, Wiley, 2002 Khoshafian/Baker: Multimedia and Imaging Databases, Morgan Kaufmann, 1996 Sometimes: original papers (on our Web page) Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 3 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 4 0. Organizational Issues Course Web page Contains slides, excercises, related papers and a video of the lecture Any questions? Just drop us an 1. Introduction 1. Introduction 1.1 What are multimedia databases? 1.2 Multimedia database applications 1.3 Evaluation of retrieval techniques Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 5 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 6 1

2 1.1 Multimedia Databases What are multimedia databases (MMDB)? Databases + multimedia = MMDB Key words: databases and multimedia We already know databases, so what is multimedia? 1.1 Basic Definitions Multimedia The concept of multimedia expresses the integration of different digital media types The integration is usually performed in a document Basic media types are text, image, vector graphics, audio and video Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 7 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Data Types Text Text data, Spreadsheets, , Image Photos (Bitmaps), Vector graphics, CAD, Audio Speech- and music records, annotations, wave files, MIDI, MP3, Video Dynamical image record, frame-sequences, MPEG, AVI, 1.1 Documents Earliest definition of information retrieval: Documents are logically interdependent digitally encoded texts Extension to multimedia documents allows the additional integration of other media types as images, audio or video Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 9 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Documents Document types Media objects are documents which are of only one type (not necessarily text) Multimedia objects are general documents which allow an arbitrary combination of different types Multimedia data is transferred through the use of a medium 1.1 Basic Definitions Medium A medium is a carrier of information in a communication connection It is independent of the transported information The used medium can also be changed during information transfer Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 11 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 12 2

3 1.1 Medium Example 1.1 Medium Classification Book Communication between author and reader Independent from content Hierarchically built on text and images Reading out loud represents medium change to sound/audio Based on receiver type Visual/optical medium Acoustic mediums Haptical medium through tactile senses Olfactory medium through smell Gustatory medium through taste Based on time Dynamic Static Multimedia Databases Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 13 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Multimedia Databases We now have seen What multimedia is And how it is transported (through some medium) But why do we need databases? Most important operations of databases are data storage and data retrieval 1.1 Multimedia Databases Persistent storage of multimedia data, e.g.: Text documents Vector graphics, CAD Images, audio, video Content-based retrieval Efficient content based search Standardization of meta-data (e. g., MPEG-7, MPEG-21) Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 15 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Multimedia Databases 1.1 Historical Overview Stand-alone vs. database storage model? Special retrieval functionality as well as corresponding optimization can be provided in both cases But in the second case we also get the general advantages of databases Declarative query language Orthogonal combination of the query functionality Query optimization, Index structures Transaction management, recovery Retrieval procedures for text documents (Information Retrieval) Relational Databases and SQL Presence of multimedia objects intensifies SQL-92 introduces BLOBs First Multimedia-Databases Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 17 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 18 3

4 1.1 Commercial Systems Relational Databases use the data type BLOB (binary large object) Uninterpreted data Retrieval through metadata like e.g., file name, size, author, Object-relational extensions feature enhanced retrieval functionality Semantic search IBM DB2 Extenders, Oracle Cartridges, Integration in DB through UDFs, UDTs, Stored Procedures, 1.1 Requirements Requirements for multimedia databases (Christodoulakis, 1985) Classical database functionality Maintenance of unformatted data Consideration of special storage and presentation devices Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 19 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Requirements 1.1 Requirements To comply with these requirements the following aspects need to be considered Software architecture new or extension of existing databases? Content addressing identification of the objects through content-based features Performance improvements using indexes, optimization, etc. User interface how should the user interact with the system? Separate structure from content! Information extraction (automatic) generation of content-based features Storage devices very large storage capacity, redundancy control and compression Information retrieval integration of some extended search functionality Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 21 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Retrieval 1.1 Retrieval Retrieval means the choice between data objects which can be based on a SELECT condition (exact match) or a defined similarity connection (best match) Retrieval may cover the delivery of the results to the user, too Closer look at the search functionality Semantic search functionality Orthogonal integration of classical and extended functionality Search does not directly access the media objects Extraction, normalization and indexing of contentbased features Meaningful similarity/distance measures Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 23 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 24 4

5 1.1 Content-based Retrieval Retrieve all images showing a sunset! 1.1 Schematic View Usually 2 main steps Example: image databases Image collection Digitization Image analysis and feature extraction Creating the database Image database What exactly do these images have in common? Image query Digitization Querying the database Image analysis and feature extraction Similarity search Search result Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 25 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Detailed View 1.1 More Detailed View Query Result Query Result 3. Query preparation Query plan & feature values 5. Result preparation Result data Query preparation Normalization Segmentation Optimization Feature extraction Feature values Query plan Relevance feedback Result preparation Medium transformation Format transformation Result data 4. Similarity computation & query processing Similarity computation Query processing Feature values 2. Extraction of features Raw data Raw & relational data MM-Database 1. Insert into the database MM-Objects + relational data Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 27 Feature values MM-Database Relational DB Metadata Feature index BLOBs/CLOBs Profile Structure data Feature extraction Pre-processing Feature recognition Decomposition Feature preparation Normalization Segmentation MM-Objects Relational data Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 28 Lots of multimedia content on the Web Social networking e.g., Facebook, MySpace, Hi5, Orkut, etc. Photo sharing e.g., Flickr, Photobucket, Imeem, Picasa, etc. Video sharing e.g., YouTube, Megavideo, Metacafe, blip.tv, Liveleak, etc. Cameras are everywhere In London there are at least 500,000 cameras in the city, and one study showed that in a single day a person could expect to be filmed 300 times Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 29 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 30 5

6 10/27/2009 Picasa face recognition Picasa, face recognition example 31 Picasa example Sample Scenario Sample Scenario Consider a police investigation of a large-scale drug operation Possible generated data: Possible queries Image query by example: police officer has a photograph and wants to find the identity of the person in the picture Video data captured by surveillance cameras Audio data captured Image data consisting of still photographs taken by investigators Structured relational data containing background information Geographic information system data 32 Picasa, learning phase Query: retrieve all images from the image library in which the person appearing in the (currently displayed) photograph appears Image query by keywords: police officer wants to examine pictures of Tony Soprano Query: retrieve all images from the image library in which Tony Soprano appears"

7 1.2 Sample Scenario Video Query: Query: Find all video segments in which Jerry appears By examining the answer of the above query, the police officer hopes to find other people who have previously interacted with the victim 1.2 Sample Scenario Heterogeneous Multimedia Query: Find all individuals who have been photographed with Tony Soprano and who have been convicted of attempted murder in New Jersey and who have recently had electronic fund transfers made into their bank accounts from ABC Corp. Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 37 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Characteristics 1.2 Example Basic difference Static High number of search queries (read access), few modifications of the data state Dynamic Often modifications of the data state Active Database functionality lead to application operations Passive Database reacts only at requests from outside Standard search Queries are answered through the use of metadata e.g., Google-image search Retrieval functionality Content based search on the multimedia repository e.g., Picasa face recognition Passive static retrieval Art historical use case Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 39 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Example Possible hit in a multimedia database 1.2 Example Active dynamic retrieval Wetter warning through evaluation of satellite photos Extraction Typhoon-Warning for the Philippines Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 41 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 42 7

8 1.2 Example 1.2 Example Standard search Queries are answered through the use of metadata e.g., Google-image search Retrieval functionality Content based e.g., Picasa face recognition Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 43 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Towards Evaluation 1.3 Evaluating Efficiency Basic evaluation of retrieval techniques Efficiency of the system Efficient utilization of system resources Scalable also over big collections Effectivity of the retrieval process High quality of the result Meaningful usage of the system Weighting is application specific Characteristic values to measure efficiency are e.g.: Memory usage CPU-time Number of I/O-Operations Response time Depends on the (Hardware-) environment Goal: the system should be efficient enough! Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 45 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Evaluating Effectivity 1.3 Evaluating Effectivity Measuring effectivity is more difficult and always depending on the query Goal: define some query-dependent evaluation measures! Objective quality metrics Result evaluation based on the query Independent from used querying interface and retrieval procedure Leads to comparability of different systems/algorithms Effectivity can be measured regarding some explicit query Main focus on evaluating the behavior of the system with respect to a query Relevance of the result set But effectivity also needs to consider implicit information needs Main focus on evaluating the usefulness, usability and user friendliness of the system Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 47 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 48 8

9 1.3 Relevance 1.3 Usefulness (Pertinence) To evaluate a retrieval system over some query, each document will be classified binary as relevant or irrelevant with respect to the query This classification is performed by experts The response of the system to the query will be compared to this manual classification Compare the obtained response with the ideal result Subjective measure estimating to what degree the information need of the user is satisfied Difficult to measure (empirical studies) Questionable instrument for comparing procedures/systems Attention: useful documents can be irrelevant when considering the query (serendipity) In this lecture: explicit query evaluation measures only! Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 49 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Involved Sets 1.3 False Positives collection Irrelevant documents, classified as relevant by the system False alarms, false drops, searched for (= relevant) found (= query result) Needlessly increase the result set Usually inevitable (ambiguity) Can be easily eliminated by the user Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 51 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig False Negatives 1.3 Remaining Sets Relevant documents classified by the system as irrelevant False dismissals Dangerous, since they can not be detected easily by the user Does the collection contain better documents? False positives are usually not as bad as false negatives Correct positives (correct alarms) All documents correctly classified by the system as relevant Correct negatives (correct dismissals) All documents correctly classified by the system as irrelevant All sets are disjunctive and their reunion is the entire document collection Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 53 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 54 9

10 1.3 Overview 1.3 Interpretation Systemevaluation Userevaluation relevant irrelevant {Relevant results} = fd + ca {Retrieved results} = ca + fa collection relevant irrelevant ca fa fd cd fd searched for ca fa found cd Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 55 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Precision Precision measures the ratio of correctly returned documents relative to all returned documents P = ca / (ca + fa) Value between [0, 1] (1 representing the best value) High number of false alarms mean worse results 1.3 Recall Recall measures the ratio of correctly returned documents relative to all relevant documents R = ca / (ca + fd) Value between [0, 1] (1 representing the best value) High number of false drops mean worse results Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 57 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Precision-Recall Analysis Both measures only make sense, if considered at the same time E.g., get perfect recall by simply returning all documents, but then the precision is extremely low Can be balanced by tuning the system E.g., smaller result sets lead to better precision rates at the cost of recall Usually the average precision-recall of more queries is considered (macro evaluation) 1.3 Actual Evaluation Alarms (returned elements) can be easily divided in ca and fa Precision is easy to calculate Dismissals (not returned elements) are not so trivial to divide in cd und fd, because the entire collection has to be classified Recall is difficult to calculate Standardized Benchmarks Provided connections and queries Annotated result sets Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 59 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 60 10

11 1.3 TREC 1.3 Example Text REtrieval Conference De-Facto-Standard since 1992 Establish average precision for 11 fixed recall points (0; 0,1; 0,2; ; 1) according to defined procedures (trec_eval) Different tracks, extended also for video data, Web retrieval and Question Answering Other initiatives: e.g., CLEF (cross-language retrieval) or INEX (XML-Documents) Query Q 1 Q 2 fa 8 2 ca fd cd 4 8 Average P 0,2 0,8 0,5 R 0,25 0,8 0,525 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 61 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig Representation Next lecture Precision-Recall-Graphs System 1 System 2 System 3 Average precision of the system 3 at a recall-level of 0,2 Which system is the best? What is more important: recall or precision? Retrieval of images by color Introduction to color spaces Color histograms Matching Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 63 Multimedia Databases Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 64 11

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