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CLEF 2007 Medical Image Annotation Task Budapest, September 19-21 2007. An SVM-based Cue Integration Approach. Tatiana Tommasi, Francesco Orabona, Barbara Caputo IDIAP Research Institute, Centre Du Parc, Av. Des Pres-Beudin 20, martigny, Switzerland. Overview. Problem Statement Features - PowerPoint PPT Presentation
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CLEF 2007CLEF 2007
Medical Image Annotation TaskMedical Image Annotation Task
Budapest, September 19-21 2007Budapest, September 19-21 2007
Tatiana Tommasi, Francesco Orabona, Barbara Caputo
IDIAP Research Institute, Centre Du Parc, Av. Des Pres-Beudin 20, martigny, Switzerland
OverviewOverview
• Problem Statement
• Features
• Classifier
• Results
• Conclusions
Problem StatementProblem Statement
Automatic Image Annotation task’s GOAL: classify a test set of 1000 medical images, having a training set of 11000 medical images.
IRMA db: Radiographic images divided into 116 classes according to the IRMA code
IRMA code consists of four independent axes: modality
- body region - body orientation - biological system
Score: errors annotation depends on the level of the hierarchy at which the error is made - a greater penalty is applied for incorrect classification than for a less specific classification in the hierarchy
Local Features – SIFTLocal Features – SIFT
Scale Invariant Feature Transform : local feature descriptor invariant to changes in
- illumination
- image noise
- rotation
- scaling
- minor changes in viewing direction
SIFT points = local maxima of the scale space
Really the most informative for a classification task ??
• Dense random sampling of the SIFT points better than interest point detectors
• Radiographs : low contrast images
No keypoint orientation
SIFT extracted at only one octave
Vocabulary of Visual Words - SIFTVocabulary of Visual Words - SIFT
30 SIFT points extracted from each of the 12000 images
K-means algorithm with K=500
Define a vocabulary of 500 words
1500 points
Feature Vector of 2000 elements
Global Features – Raw PixelsGlobal Features – Raw Pixels
Images resized to 32x32 pixels
gray value of each pixel normalized to have sum 1
Feature Vector of 1024 elements
…..
Support Vector MachineSupport Vector Machine
Training data: (x1,y1) ,…,(xm,ym) xi N ,yi {-1, +1}
Optimal separating hyperplane: that with maximum distance to the closest points in the training set
( · x +b = 0)
f(x) = sign(i=1…m i yi · xi + b)
the xi with non zero i are SUPPORT VECTORS
Non linear SVM: x (x) K(x,y)= (x) · (y)
instead of ( · x)
Chi-square kernel: K(x,y)= exp{- ² (x,y)} ² = i { (||xi-yi||) ² / ||xi+yi|| }
Multi-Class SVMMulti-Class SVM
one-vs-all - for c classes employs c classifiers.
e.g. 3 classes:
margin(x) 1 vs 2,3 margin(x) 2 vs 1,3 margin(x) 3 vs 1,2
x class max(margin)
one-vs-one - for c classes employs c(c-1)/2 classifiers.
e.g. 3 classes:
(x) 1 vs 2 class 2
(x) 1 vs 3 class 3
(x) 2 vs 3 class 3
x class 3
Discriminative Accumulation Scheme - DASDiscriminative Accumulation Scheme - DAS
Main idea: information from different cues can be summed together
M object classes, each with Nj training images {Iij} i=1,…, Nj j=1,…M
For each image we extract a set of P different cue Tp = Tp(Iij), p = 1,…,P
So for an object j we have P new training sets {Tp(Iij)} i=1…Nj
I’ = test image, M 2,
cue the distance from the separating hyperplane is
Dj(p) = i=1…mjp
ijp
yijKp(Tp (Ii j),Tp(I’))+bj
p
Having all the distances for all the j objects and p cues, the image I’ is classified through
j*=argmax j=1…M {p=1…P ap Dj(p) } ap +
Discriminative Accumulation Scheme - DASDiscriminative Accumulation Scheme - DAS
Example with two cues: class1 : 2 images class2 : 3 images class3 : 2 images
Multi Cue Kernel - MCKMulti Cue Kernel - MCK
Main idea: a new kernel which combines different features extracted from images through a positively weighted linear combination of kernels each of them dealing with only one feature
KMC({Tp (Ii)}p,{Tp(I’)}p) = p=1…P ap Kp(Tp(Ii),Tp(I’))
It is possible to
• optimize the weighting factors ap and all the kernel parameters together;
• works both with one-vs-all and one-vs-one SVM extension to the multiclass problem
Experiments Experiments
Single feature Evaluation
- 5 random and disjoint train/test splits of 10000/1000 images are extracted
- best parameters that giving the lowest average score on the 5 splits
- experiments with one-vs-one and one-vs-all SVM multiclass extension
SIFT features outperform the raw pixel ones
Experiments Experiments
Cue Integration
DAS - distances from the separating hyperplanes associated with the best results of the previous step
- cross validation used only to search the best weights for cue integration
MCK - cross validation applied to look for the best kernel parameters and the best feature’s weights at the same time
In both cases weights varied form 0 to 1
Results Results
When the label predicted by the raw pixel is wrong the true label is far from the top of the decision ranking
Results Results
The best feature weight for SIFT results higher than that for raw pixels for all the integration methods
The number of support vectors for the best MCK run is higher than that used by the correspondent single cue SIFT but lower than that used by PIXEL and DAS.
Results Results
First, second and third column contain examples of images misclassified by one of the two cues but correctly classified by DAS and MCK
The fourth column shows an example of an image misclassified by both cues and by DAS but correctly classified by MCK
Conclusions and Future WorkConclusions and Future Work
We would like to …
use various types of local and global descriptors, to select the best features for the task;
add shape descriptors in our fusion schemes, which should result in a better performance;
exploit the natural hierarchical structure of the data.
Cue integration pays off
Cross Validation pays off