multimedia alicja wieczorkowska multimedia database systems and gis

Multimedia

Alicja Wieczorkowska

Multimedia database systems

and GIS

A.Wieczorkowska /482

Databases

• Relational databases• Object-Oriented Databases


Organizing multimedia content

• Multidimensional data structures• Image databases• Text/document databases• Video databases• Audio databases• Multimedia databases


Multidimensional data structures

• k-d trees• Point Quadtrees• MX-Quadtrees• R-Trees


k-d trees

• k-d tree is used to store k-dimensional point data such as that shown below


2-d trees

• Each node has a certain record structure

nodetype=recordINFO: infotypeXVAL: realYVAL: realLLINK: nodetypeRLINK: nodetype

end

INFO XVAL YVAL

LLINK RLINK


2-d trees

• 2-d tree is any binary tree satisfying the following conditions:– If N is a node in the tree such that level(N) is even,

then every node M in the subtree rooted at N.LLINK has the property that M.XVAL<N.XVAL, and every node P in the subtree rooted at N.RLINK has the property that P.XVAL >=N.XVAL

– If N is a node in the tree such that level(N) is odd, then every node M in the subtree rooted at N.LLINK has the property that M.YVAL<N.YVAL, and every node P in the subtree rooted at N.RLINK has the property that P.YVAL >=N.YVAL


2-d trees - example


Point Quadtrees

• Used to represent point data in 2D spaces• always splits regions into 4 parts• Node:

qtnodetype = recordINFO: infotype;XVAL: real;YVAL: real;NW, SW, NE, SE: qtnodetype

end


Point Quadtrees


MX-Quadtrees• The shape (and height) of of the tree is

independent of the number of nodes present in the tree, as well as the order of insertion of these nodes

• We assume that the map being represented is split up into a grid of size 2k x 2k


MX-Quadtrees


MX-Quadtrees

Child XLB XUB YLV YUB

NW N.XLB N.XLB+w/2 N.YLB+w/2 N.YLB+w

SW N.XLB N.XLB+w/2 N.YLB N.YLB+w/2

NE N.XLB+w/2 N.XLB+w N.YLB+w/2 N.YLB+w

SE N.XLB+w/2 N.XLB+w N.YLB N.YLB+w/2

W=N.XUB-N.XLB, root: XLB=0, XUB=2k, YLB=0, YUB=2k


R-Trees

• Used to store rectangular regions of an image or map

• particularly useful in storing very large amounts of data on disc

• each R-tree has an associated order, which is an integer K; each nonleaf R-tree node contains a set of at most K rectangles and at least rectangles 2/K


R-Trees• Intuitively, each nonleaf node in R-tree, with the

exception of the root, must be at least half full• the height of the R-tree used to store a collection

of rectangles is usually quite small• a rectangle is either a “real” rectangle or a group

rectangle• Structure:

rtnodetype = recordRec1 ,…, RecK : rectangle

P1 ,…, PK : rtnodetype


R-Trees


Comparison of different data structures

• Point quadtrees are very easy to implement– Point containing k nodes may have height k

• K-d trees are very easy to implement– In general point containing k nodes may have height k,

in practice path lengths from root to leaf longer than in point quadtrees

• MX-quadtrees have guaranteed height of at most O(n), where n is the number of records in the tree

• The same applies to R-trees; fewer disc accesses– Bounding rectangles may overlap, so we might follow

multiple paths down the tree• R-trees are generally preferred over k-d trees


Selected commercial systems

• Informix– MapInfo Geocoding

• Oracle Universal Server• Intergraph• VISION (Vision International – Sybase’s

partner)• ARC/INFO (ESRI)


Geographic Information Systems

• GIS is a System of computer software, hardware and data, and personnel to help manipulate, analyze and present information that is tied to a spatial location– spatial location – usually a geographic location– information – visualization of analysis of data– system – linking software, hardware, data– personnel – a thinking explorer who is key to

the power of GIS• http://www.gis.com/• http://www.gis.com/whatisgis/whatisgis.pdf

http://www.gis.com/

http://www.gis.com/whatisgis/whatisgis.pdf







Image databases• Querying image databases is often fundamentally

different from querying textual databases and is further complicated by the usually imprecise techniques for image analysis

• describing the content of an image can be done either automatically or manually; in both cases, structures to store the results are needed

• image databases can be implemented as:– extensions of the relational model– using n-dimensional data structures– using image transformations


Image representations• Raw images

– shape descriptor - describes the shape/location of the region within the object is located

– property descriptor - for example RGB values

• Compressed Image Representations– DFT– DCT– wavelet transform


Image databases• Image processing: segmentation

– homogeneous regions with respect to some homogeneity predicate

• over k% of cells have the same color• use a “baseline” function and a maximal permissible noise level

• Similarity-based retrieval


Similarity

• Metric approach– we assume a distance metric; given an input image i, we

look for the “nearest” neighbor of i in the image archive• Transformation approach

– the users should specify what they consider to be similar– for 2 given objects o1, o2, the level of dissimilarity

between o1 and o2 is proportional to the (minimum) cost of transforming o1 into o1 or vice versa


Representing image DBs

• Relational model• with spatial data structures

– R-trees, generalized R-trees, etc.• Using image transformations

– DCT– DFT



• Knoware (Camrax)– for artwork, real estate, and personnel management

systems• Informix

– images indexed using specialized techniques– visual image retrieval datablade

• DB2 (IBM)– facilitate retrieval of data based on attributes of images

such as colors, texture and so on


Text/document databases• Synonymy:

– various words may possess the same meaning• polysemy:

– the same word may mean many different things in different contexts

• evaluating the performance of text retrieval systems

all

relevant

returned


Text/document databases

• Precision - of algorithm A with respect to the predicate relevant and the test set Dtest is Pt% for topic t

– how many of the answers returned by the algorithm are in fact correct

• Recall– how many of the right documents are in fact

retrieved by the query

)})(|({1})),()(|({1

100tAdDdcard

trueisdtrelevanttAdDdcardP

test

testt

})),(|({1})),()(|({1

100trueisdtrelevantDdcard

trueisdtrelevanttAdDdcardR

test

testt


Text/document databases

• Stop list - set of words that are deemed “irrelevant”, even though they may appear frequently (the, and, for)

• Word stems (drug, drugged, drugs)• Frequency Tables - in a frequency table

FreqT, each document dn is represented by the n-th column, and the occurrence of each term/word tn is represented by n-th row


Video databases

• Video is a sequence of images• organizing video content:

– which aspect to choose– content extraction

• segmentation methods usually require some restricted conditions


Which aspects of video to store?

• The content of a video v is described by:– OBJ - set of objects of interest in the video v– AC - set of activities of interest in v - function, tells which objects and which

activities are associated with any given frame f• example:

– educational databases: • lecturers, topics, • lecture, questions, answers


Indexing video content

• Compact representation of the video content:– Frame segment trees

• 2 arrays are created: object array and activity array, ordered linked lists of pointers

• the frame segment tree is constructed from the segment table

– R-segment trees• each R-tree node has a special structure to specify, for

each rectangle, which object or activity is associated with it


Video standards

• MPEG• Cinepak• MPEG-2


Audio databases

• Audio signals are certain kinds of continuous analytic functions

• compression into discrete representation– transforms: DFT, DCT

• indexing audio data– TV-trees – telescope vector trees - can be used

(technique applied in text/document databases)


Audio databases

• Metadata to represent audio content:– singers, score, transcript

• Segmentation– split up the audio signal into relatively

homogeneous windows– window size specified a priori or user-defined

• Feature extraction– intensity, loudness, pitch, brightness– statistical properties: variance, correlation



• Audio databases are still in their infancy• Informix – MuscleFish • DB2 (IBM)



• Multidimensional data structures• Image databases• Text/document databases• Video databases • Audio databases• Multimedia databases


• Architectures for content organization– the principle of autonomy – the principle of uniformity– hybrid organization

Multimedia databases


• Discs • CD-ROM• Tapes

Source• V.S. Subrahmanian: Principles of

Multimedia Database Systems, Morgan Kaufmann Publishers, San Francisco, CA, USA, 1998

Storage

multimedia alicja wieczorkowska multimedia database systems and gis

Documents