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Chad Voegele

Chad Voegele - University of Texas at Austin

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Page 1: Chad Voegele - University of Texas at Austin

Chad Voegele

Page 2: Chad Voegele - University of Texas at Austin

Selective Search for Object Recognition

Page 3: Chad Voegele - University of Texas at Austin

Outline

1. Individual contribution of region similarity measures2. Importance of good base segmentation3. Box overlap correspondence to recognition

3

Page 4: Chad Voegele - University of Texas at Austin

Outline

1. Individual contribution of region similarity measures2. Importance of good base segmentation3. Box overlap correspondence to recognition

4

Page 5: Chad Voegele - University of Texas at Austin

Similarity Measures

Color

Texture

Size

Fill

5

Page 6: Chad Voegele - University of Texas at Austin

Color Similarity

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Page 7: Chad Voegele - University of Texas at Austin

Color Similarity

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Page 8: Chad Voegele - University of Texas at Austin

Color Similarity

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Page 9: Chad Voegele - University of Texas at Austin

Color Similarity

9

Page 10: Chad Voegele - University of Texas at Austin

Texture Similarity

10

Page 11: Chad Voegele - University of Texas at Austin

Texture Similarity

11

Page 12: Chad Voegele - University of Texas at Austin

Texture Similarity

12

Page 13: Chad Voegele - University of Texas at Austin

Texture Similarity

13

Page 14: Chad Voegele - University of Texas at Austin

Size Similarity

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Page 15: Chad Voegele - University of Texas at Austin

Size Similarity

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Page 16: Chad Voegele - University of Texas at Austin

Size Similarity

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Page 17: Chad Voegele - University of Texas at Austin

Size Similarity

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Page 18: Chad Voegele - University of Texas at Austin

Fill Similarity

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Page 19: Chad Voegele - University of Texas at Austin

Fill Similarity

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Page 20: Chad Voegele - University of Texas at Austin

Fill Similarity

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Page 21: Chad Voegele - University of Texas at Austin

Outline

1. Individual contribution of region similarity measures2. Importance of good base segmentation3. Box overlap correspondence to recognition

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Page 22: Chad Voegele - University of Texas at Austin

Success Case

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Page 23: Chad Voegele - University of Texas at Austin

Success Case

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Page 24: Chad Voegele - University of Texas at Austin

Failure Cases

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Page 25: Chad Voegele - University of Texas at Austin

Failure Cases

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Page 26: Chad Voegele - University of Texas at Austin

Failure Cases

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Page 27: Chad Voegele - University of Texas at Austin

Failure Cases

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Page 28: Chad Voegele - University of Texas at Austin

Outline

1. Individual contribution of region similarity measures2. Importance of good base segmentation3. Box overlap correspondence to recognition

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Page 29: Chad Voegele - University of Texas at Austin

Box Overlaps

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Page 30: Chad Voegele - University of Texas at Austin

Box Overlaps

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1. ostrich2. Arabian camel3. black stork4. llama

1. zebra2. tiger3. triceratops4. tiger cat

1. chime, gong2. plate rack3. snake fence4. organ

Page 31: Chad Voegele - University of Texas at Austin

Box Overlaps

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Score: 0.851. ostrich2. black stork3. vulture4. Arabian camel

Score: 0.951. chime, gong2. snake fence3. organ4. thatched roof

Score: 0.891. zebra2. tiger3. trcieratops4. tiger cat

Page 32: Chad Voegele - University of Texas at Austin

Box Overlaps

32

Score: 0.501. zebra2. tiger3. gazelle4. impala

Score: 0.501. ostrich2. llama3. Arabian camel4. vulture

Score: 0.501. snake fence2. plate rack3. bannister4. picket fence

Page 33: Chad Voegele - University of Texas at Austin

Box Overlaps

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Score: 0.511. zebra2. fire screen3. gazelle4. patas (monkey)

Score: 0.501. borzoi (dog)2. timber wolf3. red wolf4. badger

Score: 0.501. plate rack2. chime, gong3. shoji (Japanese door)4. window shade

Page 34: Chad Voegele - University of Texas at Austin

Issues

● Author released half p-code, half m-code.● Optimal segmentation coloring problem.● AlexNet output very sensitive to image patch size.

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Page 35: Chad Voegele - University of Texas at Austin

Sources

Code- http://koen.me/research/selectivesearch/ - http://caffe.berkeleyvision.org/

Pictures- PASCAL Visual Object Class Challenge 2007: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/index.html- http://artisanhardware.com/wp-content/uploads/2015/09/47c6ebf4-7a3e-4345-acb5-6a23c9b0b405.jpg - https://upload.wikimedia.org/wikipedia/en/9/93/Pacersoriginallogo.gif - http://animaliaz-life.com/data_images/koala/koala4.jpg - http://www.synlawngolf.com/wp-content/gallery/golf-installations/golf-027.jpg - https://indierevolver.files.wordpress.com/2015/07/chewbacca-han-solo-e1436634523782.jpg - http://cdn0.sbnation.com/imported_assets/196372/200803231600576549171-p2.jpeg - http://www.planetizen.com/files/images/ChicagoEl.jpg- https://www.flickr.com/photos/128888346@N02/24927420741 - https://upload.wikimedia.org/wikipedia/commons/5/52/Madrid_Zoo.jpg 35

Page 36: Chad Voegele - University of Texas at Austin

Appendix

Page 37: Chad Voegele - University of Texas at Austin

Initial Segmentations

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Page 38: Chad Voegele - University of Texas at Austin

Initial Segmentations

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Page 39: Chad Voegele - University of Texas at Austin

Initial Segmentations

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Page 40: Chad Voegele - University of Texas at Austin

Initial Segmentations

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Page 41: Chad Voegele - University of Texas at Austin

Box Overlap

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1. oxcart2. ox3. horse cart4. zebra5. llama6. bighorn sheep7. ram8. water buffalo9. warthog

10. dogsled