Content based image retrieval
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Content-based Image Retrieval. Mei Wu Faculty of Computer Science Dalhousie University. Motivation. The huge amount of images, resulting from the fast development of multimedia and the wide spread of internet, makes user-labelled annotation method “mission impossible”.

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Content-based Image Retrieval

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Content based image retrieval

Content-based Image Retrieval

Mei Wu

Faculty of Computer Science

Dalhousie University


Motivation

Motivation

  • The huge amount of images, resulting from the fast development of multimedia and the wide spread of internet, makes user-labelled annotation method “mission impossible”.

  • People are seeking for automatic image retrieval methods which are based on images own contents, such as color, texture and shape, rather than manually-labelled annotations.

  • CBIR can be broadly used in areas, such as crime prevention, medical diagnosis, satellite imaging and online searching.


Cbir system architecture

CBIR System Architecture


Image content representation

8 Base GET types

GET grouping

Image content representation

Sample PGET (upper), JGET (lower)

Image Content Representation


Two samples

Two Samples

Query image

The top ten retrieved images


Experimental results

Experimental Results

Shape features comparison

Shape/color comparison


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