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Banco de Dados Multimídia

Banco de Dados Multimídia. Capítulo 4: BD Imagens UFCG/CEEI/COPIN Cláudio Baptista. BD Imagens. Motivação Grande volume de imagens disponíveis Projeto NASA EOSDIS envia Petabytes de imagens por ano! Cada cidadão norte-americanoque tiver tirado um passaporte tem sua fotografia digital

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Banco de Dados Multimídia

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  1. Banco de Dados Multimídia Capítulo 4: BD Imagens UFCG/CEEI/COPIN Cláudio Baptista

  2. BD Imagens • Motivação • Grande volume de imagens disponíveis • Projeto NASA EOSDIS envia Petabytes de imagens por ano! • Cada cidadão norte-americanoque tiver tirado um passaporte tem sua fotografia digital • Hospitais estão armazenando imagens: raios-X, Tomografia, Ressonância Magnética • Museus e bibliotecas digitais multimídia têm usado imagens digitais • Usuários têm gerado fotografias digitais

  3. BD Imagens • Em SGBD convencionais (ex. relacional), estas imagens são consultadas pelas anotações que são realizadas (metadados) • Existe a necessidade de se realizar consulta nas próprias imagens, usando metadados de baixo nível: cor, textura, forma. • Este é o propósito da técnica de Content-Based Retrieval - CBR

  4. BD Imagens • O conteúdo de uma imagem consiste de todos os objetos visíveis a uma aplicação • Este objetos têm as seguintes propriedades: • descritor de forma: forma/localização de objeto na imagem, em geral usam-se retângulos (bounding) que identificam (circundam) as formas. • descritor de propriedades: propriedades de pixels (ou grupo de pixels), ex. valores RGB ou tons-de-cinza.

  5. BD Imagens NY, NY Cadê a torre? Na figura acima os retângulos mostram a extração de formas do objeto avião. Nem sempre retângulos são suficientes, porquê?

  6. BD Imagens • Em geral, ao invés de trabalhar com cada pixel individualmente, agrupam-se estes em células. • Definição: Cada imagem I tem um par de inteiros positivos (m,n), chamado de grid de resolução da imagem. Isto divide a imagem em (m x n) células de tamanho igual. • Cada célula num dado grid consiste de uma coleção de pixels.

  7. BD Imagens • Definição: Uma propriedade de uma célula é uma tripla (nome, valor, método), onde • nome: denota o nome da propriedade • valor: é um conjunto de valores que a propriedade pode assumir • método: é o algoritmo que computa a propriedade • Ex.: Imagem preto-e-branco e outra tons de cinza (graylevel), uma propriedade de uma célula seria • (bwcolor, {b,w}, bwalgo) • (graylevel, [0,1], grayalgo)

  8. BD Imagens • Onde graylevel: • graylevel(célula) = XLBiXUBYLBjYUB findgray(i,j)/ (XUB - XLB) x (YUB - YLB) • Onde • XLB: (X Lower Bound) limite inferior X do retângulo que contorna a célula • XUB: (X Upper Bound) limite superior X do retângulo que contorna a célula

  9. BD Imagens • Definição: Uma forma de um objeto é qualquer conjunto de pontos P tais que se p,q  P, então existe uma sequência de pontos p1, …, pn em P tais que: • p = p1 e q = pn •  1in, p i+1 é um vizinho de pi , isto é, se pi = (xi,yi) e p i+1 = (xi+1,yi+1), então (xi+1,yi+1) satisfaz uma das seguintes condições: (xi+1,yi+1) = ( xi + 1, yi); (xi+1,yi+1) = ( xi - 1, yi); (xi+1,yi+1) = ( xi, yi + 1); (xi+1,yi+1) = ( xi, yi - 1); (xi+1,yi+1) = ( xi + 1, yi+1); (xi+1,yi+1) = ( xi + 1, yi-1); (xi+1,yi+1) = ( xi - 1, yi+ 1); (xi+1,yi+1) = ( xi - 1, yi - 1)

  10. BD Imagens • Definição: um retângulo é uma forma de objeto P, tal que existe inteiros XLB,XUB, YLB, YUB tais que: • P = {(x,y)| XLBxXUB & YLByYUB}

  11. BD Imagens • Imagens são representadas por bitmaps (raster images) • Um bitmap é uma matriz bidimensional formada por elementos chamados pixels • Cada pixel tem um valor numérico chamado aplitude • O número de bits utilizado para um pixel é chamado de pixel depth

  12. BD Imagens • Um pixel depth pode representar: • Um ponto preto ou branco • Um nível de cinza (imagem monocromática) • Atributos de cor (imagem colorida)

  13. BD Imagens • Compressão de Imagens: em geral representar imagens com todos os seus pixels pode ser inviável dado que podemos ter grandes valores, (1024X1024). Portanto, usamos técnicas de compressão que transforma a imagem original numa versão comprimida (menor)

  14. BD Imagens - Compressão • A criação de uma imagem comprimida consiste em duas partes: • Seleção do tamanho • Seleção do algoritmo de transformação (com perda e sem perda) • Transformada Discreta de Fourier (DFT) p1-1 p2-1 • DFT(x,y) = 1/p1p2 *   (I(a,b) x e-j(2xa/p1 + 2yb/p2)a=0 b=0 • onde j é o complexo (-1)1/2 • DFT é sem perda e preserva distância Euclidiana

  15. BD Imagens - Compressão • A Transformada Discreta do Cosseno (DCT) • DCT(i,j) = 2/(p1p2)1/2(i) * (j)r=0, p1-1 s=0, p2-1 ( cos ( ((2r + 1) x i)/2r) * cos (((2s + 1) x j)/2s)) • onde (i),(j) = 1/(2) 1/2 quando i, j = 0, ou 1 caso contrário • DCT é computado rapidamente • DCT é sem perda

  16. Formatos de Imagens • Não-comprimidas • Pgm (portable gray map) ou ppm (portable pixel map – Unix • BMP (gray e color) - Windows

  17. Formatos de Imagens • Comprimidas • GIF(Graphics Interchange Format): • Usa algorítimo de compressão LZW (Lempel-Ziv-Welch) • Taxa de compresão média : 4:1 • Pode ter animação

  18. Formatos de Imagens • Comprimidas • JPEG (Joint Photographics Expert Group) • Bom para fotos, não muito bom para pequenas imagens menores do que 100x100 pixels • Taxa de compressão 10:1 a 100:1

  19. Formatos de Imagens • Comprimidas • PNG (Portable Network Graphics) • Mais color depth (48bits) do que GIF(8bits) • 10 a 30% menores do que LZW (Gif) • Metadados textuais podem ser adicionados

  20. Ex. Imagens comprimidas jpg (6KB) Bmp (57KB) png (15KB) gif (16KB)

  21. Segmentação de Imagens

  22. Busca em Imagens • BD Tradicionais: • Designed to manage one-dimensional datasets • consisting of simple data types, such as strings and • numbers • Limited kinds of queries: exact match, • partial match, and range queries • Well-understood indexing methods: B-trees, • hashing

  23. MM queries - Características • We normally retrieve a few records from a traditional DBMS through the specification of exact queries based on the notions of “equality”. • The types of queries expected in an image/video DBMS are relatively vague or fuzzy, and are based on the notion of “similarity”.

  24. MM queries - Características The indexing structure should be able to satisfy similarity-based queries for a wide range of similarity measures.

  25. CBR • It is necessary to extract the features which are characteristics of the image and index the image on these features. Examples: Shape descriptions, texture properties.

  26. CBR • Typically there are a few different quantitative measures which describes the various aspect of each feature. Example: The texture attribute of an image can be modeled as a 3-dimensional vector with measures of directionality, contrast, and coarseness.

  27. Busca em Imagens • BD Tradicionais: • Designed to manage one-dimensional datasets • consisting of simple data types, such as strings and • numbers • Limited kinds of queries: exact match, • partial match, and range queries • Well-understood indexing methods: B-trees, • hashing

  28. MM queries - Características • We normally retrieve a few records from a traditional DBMS through the specification of exact queries based on the notions of “equality”. • The types of queries expected in an image/video DBMS are relatively vague or fuzzy, and are based on the notion of “similarity”.

  29. MM queries - Características The indexing structure should be able to satisfy similarity-based queries for a wide range of similarity measures.

  30. CBR • It is necessary to extract the featureswhich are characteristics of the image and index the image on these features. Examples: Shape descriptions, texture properties.

  31. CBR • Typically there are a few different quantitative measures which describes the various aspect of each feature. Example: The texture attribute of an image can be modeled as a 3-dimensional vector with measures of directionality, contrast, and coarseness.

  32. CBR • Multimedia require support of multi-dimensional datasets • E.g., a 256 dimensional feature vector. • That implies • Specialized kinds of queries • New indexing approaches. Two choices: • Map n-dimensional data to a single dimension and use traditional indexing structures (B-trees) • Develop specialized indexing structures

  33. Low-Dimensional Indexing Applications • Spatial Databases (GIS, CAD/CAM) • Number of dimensions: 2-4 • Spatial queries. For example: • Which objects intersect a given 2D or 3D rectangle • Which objects intersect a given object • Specialized indexing structures • quad-tree, BSP-tree, K-D-B-tree, R-tree, R+-tree, R*-tree, X-tree, …

  34. High-Dimensional (HD) Indexing Applications • Multimedia databases (Images, Sounds, Movies) • Map multimedia object to a n-dimensional point called feature vector • Number of dimensions: typically 256 - 1000 • Indexing: • Actually index only feature vectors • Data structures used: • same as for spatial databases (R-Trees, X-trees) • or, structures tailored to index specifically feature vectors(TV-Tree)

  35. HD Considerations (1) • Main problem: • In general there is no total-ordering of d-dimensional objects that preserves spatial proximity • Data comes in two forms • N-dimensional points • N-dimensional objects extended in space • Objects can have rather complex shapes (extents) • Typically abstract from the actual form and index some simpler shapes, such as Minimum Bounding Boxes (MBB) or n-dimensional hyper spheres

  36. HD Considerations (2) • “Dimensionality curse” • As the number of dimensions increases • performance tends to degrade (often exponentially) • Indexing structures become inefficient for certain kinds of queries • Performance is often CPU-bound, not just I/O-bound as in traditional DBMS

  37. HD Queries Overview • No standard algebra or query language • The set of operators strongly depends on application domain • Queries are usually expressed by an extension of SQL (e.g. abstract data types) • Although there are no standards, some queries are common

  38. Multiattribute and Spatial Indexing of Multimedia Objects • Spatial Databases: Queries involve regions that are represented as multidimensional objects. Example: A rectangle in a 2-dimensional space involves four values: two points and two values for each point. Access methods that index on multidimensional keys yield better performance for spatial queries.

  39. Multiattribute and Spatial Indexing of Multimedia Objects • Multimedia Databases: Multimedia objects typically have several attributes that characterize them. Example: Attributes of an image include coarseness, shape, color, etc. • Multimedia databases are also good candidates for multikey search structures.

  40. Similarity Based on Weighted Euclidean Distance A suitable measure of similarity between an image feature vector F and query vector Q is the weighted metric W: where A is an nxn matrix which can be used to specify suitable weighting measures.

  41. Query Types Querying in image DBMS is envisioned to be iterative in nature: • Vague Queries: Queries at the earlier stage can be very “loose”. Retrieve images containing textures similar to this sample. • K-nearest-neighbor-queries: The user specifies the number of close matches to the given query point. Retrieve 10 images containing textures directionally similar to this sample • Range queries: An interval is given for each dimension of the feature space and all the records which fall inside this hypercube are retrieved.

  42. Query Types . . . . . . . + . . . + r + .Q . . . Q Q r is large r is small range query => vague query => 3-nearest neighbor query

  43. Indexing Multimedia Objects • Can’t we index multiple features using a B+-tree ? • B+-tree defines a linear order • Similar objects (e.g., O1 and O2) can be far apart in the indexing order • Why multidimensional indexing ? • A multidimensional index defines a “spatial order” • Conceptually similar objects are spatially near each other in the indexing order (e.g., O1 and O2)

  44. Some Multidimensional Search Structures • Space Filling Curves • k-d Trees • Multidimensional Tries • Grid File • Point-Quad Trees • R Trees, R*, TV, SS • D-Trees • VA files

  45. CBIR - Content-Based Image Indexing • Keyword Approach • Problem: there is no commonly agreed-upon vocabulary for describing image properties. • Computer Vision Techniques • Problem: General image understanding and object recognition is beyond the capability of current computer vision technology.

  46. CBIR - Content-Based Image Indexing • Image Analysis Techniques • It is relatively easy to capture the primitive image properties such as • prominent regions, • their colors and shapes, • and related layout and location information within images. • These features can be used to index image data.

  47. CBIR – Features mais usadas • Edge • Region • Color • Shape • Location • Size • Texture

  48. CBIR – Edge detection horizontal edges vertical edges lena.gif norm of the gradient after thresholding after thinning

  49. CIBR - Region

  50. CBIR - Cor • We can divide the color space into a small number of zones, each of which is clearly distinct with others for human eyes. • Each of the zones is assigned a sequence number beginning from zero.

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