Download - Computer and Information Sciences

Ontology Driven Content Based
Image Retrieval
John Osborne
Paper: Popescu et al, 2007
July 30th/ 2010
Overview
• Review
– CBIR
– Ontology Definition and Example
– SCBIR
• Concept Hierarchy (Ontology)
• Picture Database
– Construction and Properties of Database
• Image Processing
– Filtering and Indexing
• RetrieveOnto System
– Modes
– Evaluation
• Conclusion and Future Directions
Content Based Image Retrieval
• Wikipedia definition
• “application of computer vision techniques to the
image retrieval problem, that is, the problem of
searching for digital images in large databases”
• Problems Addressed:
– Lack of human understandable semantics
• System here allows control of querying conceptual
neighborhoods
– Scalability
• CBIR gets more difficult as database size increases
– Interactivity
• CBIR not understandable to users
Ontology
“Specification of a conceptualization”
Leukocyte hierarchy from cell ontology
Semantic CBIR
• Use of semantics (keywords,ontologies) to aid
CBIR
– Employing ontologies to define high level ontology
• Map high level concepts to low level features
• Manually
• Use machine learning to bridge “semantic
gap”
• Use visual content, surrounding text from Web
to assist CBIR
Authors Concept Hierarchy
Placental WordNet Statistics
• Not a “true” ontology, but structure using a term hierarchy
extracted from WordNet
– Sub hierarchy of all terms under “placental”
• Not classification system, includes “dog has puppy”
– Better for their them, they want “general purpose” information
• 144 leaf nodes under dog, 10 sub-concepts of dolphin
• Hierarchy depth: 1 to 8
– Livestock terminal node from root
– Brown Swiss -> dairy cattle -> cattle -> bovine -> bovid ->
ruminant -> even-toed ungulate -> ungulate
• 1113 nodes with 841 leaf terms
• Leaf terms (and only leaf terms) have associated picture
sets
Bird Word Net Ontology
-Paper used placental, not birds
- Obviously not scientific
The Point
• “The role of the term hierarchy is to control, in
a humanly understandable fashion, the region
of the database where similar items are
retrieved”
Picture Database Construction
• Database not standardized, but created by
querying the web
• Wanted to deal with heterogeneous sources
• Employed “Ask” search engine to populate
database as it gave better precision results versus
google, yahoo or picsearch
– Did their own testing, 20 concepts (50 images per
query) and for Ask correct content (keyword
association) was 80%, Picsearch (2nd best) was 70%
Picture Database Details
•
•
•
•
Collected over 33K images
31287 after invalid links/files removed
Image filtering reduced image count to 25470
Mean # of pictures in a class: 30
– Standard deviation 23.8
– Numbers range from 0 to 147
– Well represented, lion, grizzly, poorly represented
“Doe”, “Yearling”, Pteropus capestratus
Image Processing
• Database is intended to
contain only pictures of
animals - so they
common non-animal
pictures such as faces
– Used “multi-stage
AdaBoost detector”
– Details unknown to me
– “Aardvark”
Clip Art Removal
• Clipart and scanned texts (scientific publications)
• Detect based on luminance histograms – detect
maximum and compute standard deviation with
threshold
– Not for whole picture, for 16 equals rectangles due to
uniform regions looking like clipart
• performs better than color counting
• 99.8% picture classification (11.3K+ database),
93% classification of the clipart database (5.4K+)
Image Indexing
• Index with border/interior pixel classification
from previous publication
– Quantizes each R, G and B component into 4 values
– Classify each pixel into border or interior (a pixel
whose 4 neighbors have the same quantized color is
called interior, border otherwise)
– Create 2 64 bin RGB histograms (one for border, one
for interior)
– Only look at central ¼ of the picture
• Automatic segmentation is hard
RetrievoOnto System
• 3 Major Pieces
– Conceptual Hierarchy (Ontology)
– Processed Dataset
– User interface
• UI has 2 modes (Query Mode and Answers Page)
• Query Mode
– Default query mode
• display random set of different leaf concepts
– Concept browsing query mode displays 30 random images
• Clicking on one brings up page with selection of images from that
leaf node image set
Answers Page
Query was giant panda, but sub hierarchy was defined by procynoid
Users controls via button that moves the root concept, whether to search
just the particular concept images or a larger sub hierarchy (later slide)
Traditional CBIR Answers Page
Entire database is searched
User Interface to Refine Search Space
In this case there are 8 levels for the user to move up and down from
giant_panda
Database and Filtering Evaluation
• Database evaluation
– Thirty classes covering a wide area of database
used, and 20 of those were presentdd to
reviewers who were asked if it was representative
of the class
• 86% were judged representative
• Filtering evaluation
– Similar evaluation with 200 pictures (drawings and
faces) with 35% not representative
Ontology driven versus
Classical CBIR
Depth of hierarchy was from 3 to 9
Conceptual level 1 was leaf node
Fetch large number of correct results
when restricting search to your
concept, drops off as database
expands
Some exception, blue whale did well
as did western lowland gorilla (not
shown here)
Do not show percentages
Classical CBIR really shown at right
10 images shown, results shown for 30
images but just one chart shown here
Future Directions
• Different with medical/biological ontologies
– General image matching not useful
– Image types fall into fewer classes
– Images may be different based on particular
region that is altered
• More structured ontologies
– Not just is_a relationship
– Synsets will be useless for most tasks, need rigour
• Searching is more defined