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Image Retrieval

“Information retrieval embraces the intellectual aspects of the description of information and its specification for search, and also whatever systems, techniques, or machines are employed to carry out the operation” --Calvin Moore. Image Retrieval. Image Retrieval. Popular media archives

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Image Retrieval

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  1. “Information retrieval embraces the intellectual aspects of the description of information and its specification for search, and also whatever systems, techniques, or machines are employed to carry out the operation” --Calvin Moore Image Retrieval

  2. Image Retrieval • Popular media archives • flickr.com • youtube.com • video.google.com • Textual language language is unable to adequately express the contents of imagery for efficient query.

  3. Visual Information • Metadata – • information about the object • Alphanumeric, and expressible as a schema of a relational database. • Visual Features • derived through computational processes executed on the visual object. • Image processing • Computer vision • Computational geometric routines

  4. Visual Features • Simplest visual features that can be computed are based on pixel values of raw data. • Find all images where 100th and 200th pixels are orange • Find all images having about the same color in the central region of the image as this particular image. • Find all images that are shifted images versions of this image. • Several drawbacks...

  5. Trademark Images • Vienna classification – like dewey decimal system.

  6. Artisan System • Extraction of region boundaries from bitmap images, and approximation by straight line and circular-arc segments • Reprocesss boundary representation to remove noise. • Group into families.

  7. Artisan System • Extraction and storage of global shape features • Derive a set of shape vectors from the image • The entire image • Each proximal family • Each individual family • aspect ratio • circularity • transparency • relative area • right-angleness • sharpness • complexity • directedness • straightness

  8. Information Extraction • Consider aerial images, where buildings, aircraft, roads are of interest. • Humans can identify objects, but computers have difficulty. • Lots of data would take lots of humans.

  9. Mixed mode • Most applications fall between automated pixel-oriented information models and fully human-assisted database schemes. • Color – break image up into hue and saturation • Color composition – break image into quadrants, then the above color breakdown for each quadrant. • Shape – break up into geometric shapes • Face retrieval – standardize the image to lie on the same standard coordinate system.

  10. What about video • Key frames, summarize the scene, and can be used to be searched the same as still images. • Doesn't make use of temporal and motion info. • Three kinds of motion information • movement of objects within scene, • motion of the camera, • and post-processing effects, like image warping.

  11. Visual information Query • Capture the user's mental image of the visual object. • PICQUERY+ -- “compose” visual description through textually specified attributes. • Query-by-example, provide sample image, or partial drawing.

  12. Query Refinement • Use result image from previous query to launch new query • Modify a result image with image processing tool • Erase part of the image • Change brightness level • Paint part with different color

  13. Video Query • Find video collection with a key frame like a given image • Rank the clips in the video collection in order of their similarity with a given video clip. • Cue the given video clip to the frame that is like a query image.

  14. Query Specification tools • Image-processing tool • Feature-space manipulation tool • Object specification tool • A measurement specification tool • A classification tool • A spatial arrangement tool • A temporal arrangement tool • An annotation tool • Data definition tool

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