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Looking Beyond Region Boundaries: A Robust Image Similarity Measure Using Fuzzified Region Features. Yixin Chen and James Z. Wang The Pennsylvania State University http://www.cse.psu.edu/~yixchen. Outline. Introduction A robust image similarity measure Experimental results - PowerPoint PPT Presentation
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FUZZ-IEEE 2003 1
Looking Beyond Region Boundaries:A Robust Image Similarity Measure Using Fuzzified Region Features
Yixin Chen and James Z. Wang
The Pennsylvania State Universityhttp://www.cse.psu.edu/~yixchen
FUZZ-IEEE 2003 2
Outline
Introduction
A robust image similarity measure
Experimental results
Conclusions and future work
FUZZ-IEEE 2003 3
Introduction
The driving force InternetStorage devicesComputing power
Two approachesText-based approachContent-based approach
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Text-Based Approach
Input keywords descriptions
Text-BasedImage
RetrievalSystem
Elephants
ImageDatabase
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Text-Based Approach
Index images using keywords(Google, Lycos, etc.)Easy to implementFast retrievalWeb image search (surrounding text)Manual annotation is not always availableA picture is worth a thousand wordsSurrounding text may not describe the
image
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Content-Based Approach
Index images using low-level features
Content-based image retrieval (CBIR): search pictures as pictures
CBIRSystem
ImageDatabase
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A Data-Flow Diagram
ImageDatabase
FeatureExtraction
ComputeSimilarityMeasure
Visualization
Histogram, colorlayout, sub-images,regions, etc.
Euclidean distance,intersection, shapecomparison, region matching, etc.
Linear ordering,Projection to 2-D, etc.
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Region-Based Approach
An image is viewed as a collection of regionsRegions Objects Semantics
Difficulties Image segmentationRegion matching
Our goalRobust to image segmentation
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UFM: Unified Feature Matching
MotivationHuman can identify complex objects in a
collection of points even when those points cannot always be assigned unambiguously to objects
HypothesisAllowing for blurry boundaries between
regions may increase the robustness
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UFM: Unified Feature Matching
UFM
Fuzzified region features
Region matching fuzzy logic operation
Integrate region-level similarities
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Image Segmentation
Wavelet,RGBLUV
K-means
Original Image
Segmentation Result
(Centroid, Inertia)=region features
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Image Segmentation
Segmentation examples
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Fuzzy Feature Representation
A segmented image regions {R1,…,RC}feature sets {F1,…,FC}.
Region Rj is represented by a fuzzy feature with membership function of the form
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Region Matching
Two regions “AND” or intersection of two fuzzy sets
A region and an imageAn image “Union” of all its regions
Two imagesA vector of similarities
UFM measureA convex combination of region level
similarities
FUZZ-IEEE 2003 15
Experimental Results
Query Examples from 60,000-image COREL DatabaseNatural Out-Door SceneHorsesPeopleVehicleFlag
Natural Outdoor Scene
15 matches out of 19
People
15 matches out of 19
Horses
19 matches out of 19
Flag
19 matches out of 19
Vehicle
17 matches out of 19
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Experimental Results
Robustness to Image Alterations Intensity VariationSharpness VariationColor DistortionCroppingShiftingRotation
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Experimental Results
Performance on Image CategorizationSubset of the COREL database formed by
10 image categories, each containing 100 images
Africa, Beach, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Food
Comparison with EMD-based color histogram approaches
FUZZ-IEEE 2003 20
Experimental Results
Average Precision
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Experimental Results
Robustness to Image Segmentation
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Conclusions
A robust image similarity measureFuzzified region featuresRegion matching
Good retrieval performanceRobust to image segmentationRobust to image alterations
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Future Work
Improving image segmentation
Generating fuzzy features directly form segmentation
Utilizing location information