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Seminar Medieninformatik: Object Class Detection
- 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/
- 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.
Notes de l'éditeur
- Automatische Medienanalyse und offene Daten
- Thema des Seminars: Automatische Medienanalyse -> Bilder
- Thema des Seminars: Automatische Medienanalyse -> Bilder
- Umwelt verändert sich
- Scale-invariant feature transform
- Location: Taylor Expansion der Gauß-Differenz
- Histogramm: 36 bins/Klassen => jeweils 10 Grad
- Normalerweise 16x16 -> 4x4 statt wie hier 8x8 -> 2x2 4 x 4 = 16 histograms each with 8 bins the vector has 128 -> hohe Dimension
- Histogram of oriented gradients
- 1-D Kernel Filter wie [−1,0,1]; besser als Sobel Masken
- R-HOG: rectangular C-Hog: circular
- https://www.cs.cmu.edu/~efros/courses/AP06/Papers/csurka-eccv-04.pdf
- K-means: http://www.labri.fr/perso/bpinaud/userfiles/downloads/hartigan_1979_kmeans.pdf
- Automatische Medienanalyse und offene Daten