1. Object Class Detection
Christoph Einsiedler

2. Motivation
Face recognition StreetView street address recognition
http://googleonlinesecurity.blogspot.de/2014/04/street-view-and-recaptcha-technology.html

3. Motivation
Electronic driving aids (traffic sign recognition)
Image organisation/search (automatic tagging)
http://rossel-vw.de/p_50679/de/models/cc/galerie.html

4. Problem description
Object Class Detection
Classification
Localization
Face recognition etc.
as special cases
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/

5. Problem description
robustness
Big differences between
instances of the same
category
Small differences between
instances of different
categories
complexity
Huge number of categories

6. Algorithms
Find interest points
SIFT
…
Interest point description
SIFT
HOG
…
Image description
Bag-of-features
…

7. Algorithms
SIFT
1. Scale-space extrema detection
Convolution with Gaussian filters at different scales
Calculation of differences
Points with maximal differences as keypoints
http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf

8. Algorithms
SIFT
2. Keypoint localization
Calculation of interpolatated positions
Removal of keypoints with low contrast
Removal of poorly located keypoints on edges

9. Algorithms
SIFT
3. Orientation assignment
Gradients of Gaussian smoothed image are considered (scale invariance)
Magnitudes and directions are put into a histogram
Orientation of the highest peak is assigned (rotation invariance)

10. Algorithms
SIFT
4. Keypoint descriptor
(illumination, viewing angle,… invariance)

11. Algorithms
Find interest points
SIFT •
…
Interest point description
SIFT •
HOG
…
Picture description
Bag-of-features
…

12. Algorithms
HOG
1. Gamma/Color normalization
Greyscale, RGB or LAB tested
Not neccessary
http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf

13. Algorithms
HOG
2. Gradient computation
Different masks tested (e.g. sobel masks)
1-D centered mask best

14. Algorithms
HOG
3. Orientation binning
Edge orientation histogram for each cell of the image
Orientations grouped into 9 bins (0-180°)

15. Algorithms
HOG
4. Normalization and descriptor blocks
Image divided into blocks (R-HOG, C-HOG)
Normalization
Aggregation into one vector

16. Algorithms
Find interest points
SIFT •
…
Interest point description
SIFT •
HOG •
…
Picture description
Bag-of-features
…

17. Algorithms
Bag-of-features
origins in document classification
later also used for object class detetcion in images
http://www.dtic.mil/dtic/tr/fulltext/u2/a307731.pdf

18. Algorithms
Bag-of-features
Clustering
create signatures for images
http://www.vision.caltech.edu/html-files/EE148-2005-Spring/pprs/dorko_schmid_obj_class_rec.pdf

19. Algorithms
Find interest points
SIFT •
…
Interest point description
SIFT •
HOG •
…
Picture description
Bag-of-features •
…

20. Evaluation
Comparability not easy
Pascal VOC often used
Benchmark (training data, test data)
Images from Flickr
Manually annotated
Annual competitions

21. Evaluation
Classification/Detection
Competitions
Classification
Localization
Segmentation Competition
Action Classification Competition
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/

22. Evaluation
Classification/detection competition
20 classes of objects:
Class Example image 1 Example image 2
aeroplane
bicycle
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/

23. Evaluation
Class Example image 1 Example image 2
bird
boat
bottle
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/

24. Evaluation
Class
bus
car
cat
chair
cow
diningtable
dog
horse
Class
motorbike
person
pottet plant
sheep
sofa
diningtable
train
tv/monitor

25. Evaluation
Evaluation measures:
Recall
Precision
Average Precision
http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/

26. Evaluation
Pascal VOC 2012 results
algorithm mean aero
plane
bicycle bird boat bottle bus car cat chair cow dining
table
dog horse motor
bike
person pottet
plant
sheep sofa train tv/
moni-
tor
NUSPL_CTX_
GPM_SCM
82.2 97.3 84.2 80.8 85.3 60.8 89.9 86.8 89.3 75.4 77.8 75.1 83.0 87.5 90.1 95.0 57.8 79.2 73.4 94.5 80.7
NUSPSL_CTX_GPM 78.6 95.5 81.1 79.4 82.5 58.2 87.7 84.1 83.1 68.5 72.8 68.5 76.4 83.3 87.5 92.8 56.5 77.8 67.0 91.2 77.6
NLPR_PLS_SSVW 78.3 94.5 82.6 79.4 80.7 57.8 87.8 85.5 83.9 66.6 74.2 69.4 75.2 83.0 88.2 93.6 56.2 75.6 64.1 90.0 76.6
NUS_Context_SVM 78.3 95.3 81.5 78.9 81.8 57.5 87.3 83.7 82.3 68.4 75.0 68.5 75.8 82.9 86.7 92.7 56.8 77.7 66.1 90.7 77.1
Semi-Semantic
Visual Words &
Partial Least Sqares
78.3 94.5 82.6 79.4 80.7 57.8 87.8 85.5 83.9 66.6 74.2 69.4 75.2 83.0 88.2 93.6 56.2 75.6 64.1 90.0 76.6
NUSPSL_CTX_GPM_
SVM
76.7 94.3 78.5 76.4 80.0 57.0 86.3 82.1 81.5 65.6 74.7 66.5 73.4 81.9 85.4 91.9 53.2 74.0 65.1 89.5 76.1
CVC_UVA_UNITN 74.3 92.0 74.2 73.0 77.5 54.3 85.2 81.9 76.4 65.2 63.2 68.5 68.9 78.2 81.0 91.6 55.9 69.4 65.4 86.7 77.4
UvA_UNITN_
MostTellingMonkey
73.4 90.1 74.1 66.6 76.0 57.0 85.6 81.2 74.5 63.5 62.7 64.5 66.6 76.5 81.3 90.8 58.7 69.5 66.3 84.7 77.3
CVC_CLS 71.0 89.3 70.9 69.8 73.9 51.3 84.8 79.6 72.9 63.8 59.4 64.1 64.7 75.5 79.2 91.4 42.7 63.2 61.9 86.7 73.8
MSRA_USTC_HIGH_
ORDER_SVM
70.5 92.8 74.8 69.6 76.1 47.3 83.5 76.4 76.9 59.8 54.5 63.5 67.0 75.1 78.8 90.4 43.2 63.3 60.4 85.6 71.2

27. Thank you for your attention.