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

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.



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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