12-12-2012, 12:42 PM
Image Retrieval: Current Techniques, Promising Directions, and Open Issues
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Limitations of text-based approach
Problem of image annotation
Large volumes of databases
Valid only for one language – with image retrieval this limitation should not exist
Problem of human perception
Subjectivity of human perception
Too much responsibility on the end-user
Problem of deeper (abstract) needs
Queries that cannot be described at all, but tap into the visual features of images.
What is CBIR?
Images have rich content.
This content can be extracted as various content features:
Mean color , Color Histogram etc…
Take the responsibility of forming the query away from the user.
Each image will now be described by its own features.
CBIR – A sample search query
User wants to search for, say, many rose images
He submits an existing rose picture as query.
He submits his own sketch of rose as query.
The system will extract image features for this query.
It will compare these features with that of other images in a database.
Relevant results will be displayed to the user.
Color Layout
Need for Color Layout
Global color features give too many false positives
How it works:
Divide whole image into sub-blocks
Extract features from each sub-block
Can we go one step further?
Divide into regions based on color feature concentration
This process is called segmentation.
Segmentation issues
Considered as a difficult problem
Not reliable
Segments regions, but not objects
Different requirements from segmentation:
Shape extraction: High Accuracy required
Layout features: Coarse segmentation may be enough
IBM’s QBIC
QBIC – Query by Image Content
First commercial CBIR system.
Model system – influenced many others.
Uses color, texture, shape features
Text-based search can also be combined.
Uses R*-trees for indexing