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Introduction: CBIR

Introduction: CBIR. CBIR: C ontent- B ased I mage R etrieval Image databases exist for storing art collections, satellite images, medical images, and general collections of photographs.

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Introduction: CBIR

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  1. Introduction: CBIR • CBIR: Content-Based Image Retrieval • Image databases exist for storing art collections, satellite images, medical images, and general collections of photographs.

  2. Art collection users: to find work by a certain artist or to find out who painted a particular image they have seen. • Medical database users: medical students studying anatomy or doctors looking for sample instances of a given disease. • General collections might be accessed by illustrators looking for just the right picture for an article or book.

  3. Image databases can be huge, containing hundreds of thousands or millions of images. • In most cases they are only indexed by keywords that have to be decided upon and entered into the database system by a human categorizer. • Images can also be retrieved according to their content such as color distributions, texture, region shapes, or object classification.

  4. Image Database Examples • QBIC: retrieve images based on visual content including color percentages, color layout, and texture. Fine Arts Museum of San Francisco have provided QBIC access to their image database, a collection of digital image paintings.

  5. Query-by-Example • Users show the system a sample image or paint interactively on the screen, or just sketch the outline of an object. The system should be able to return similar images or images containing similar objects.

  6. Image Distance Measures • Color similarity • Texture similarity • Shape similarity • Object and relationship similarity

  7. Color Similarity • They compare the color content of one image with the color content of a second image or of a query specification. • QBIC allows users to specify a query in terms of color percentages. The user chooses up to five colors from a color table and indicate the desired percentage of each color. QBIC looks for images that are closest to having these color percentages. The particular placement of the colors within the image is not a factor in the search.

  8. Color Histogram Matching

  9. Texture Similarity

  10. Shape Similarity

  11. Relational Similarity

  12. Texture

  13. Structural Approach • Texture is a set of primitive texels in some regular or repeated relationship.

  14. Statistical Approach • Texture is a quantitative measure of the arrangement of intensities in a region. • GLCM: gray-level co-occurrence matrix. The value of C(i, j) indicates how many times value i co-occurs with value j in some designated spatial relationship.

  15. GLCM • Let d be a displacement vector (dr, dc) where dr is a displacement in rows and dc is a displacement in columns. The GLCM Cdfor image I is defined by Cd (i, j)=|{(r, c)| I(r, c)= i and I(r+dr, c+dc = j}|

  16. Normalized and Symmetric GLCM

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