1. Edge boxes: Locating object proposals from edges
Mooyeol Baek
Zitnick, C. Lawrence, and Piotr Dollár. "Edge boxes: Locating object proposals from edges." Computer Vision–ECCV Springer International Publishing,

2. Object proposals Finds general candidates of object efficiently
CV lab. seminar

3. Low level components for object proposal
Segmentation for detection
Superpixel
Edge map
Hard to find
Whole-video processing은 영상이 길면 메모리를 너무 많이 필요로 한다. 그래서 긴 비디오에서는 차라리 frame-by-frame processing을 이용하기도 한다. (Lee, Y.J., Kim, J., Grauman, K.: Key-segments for video object segmentation. In: ICCV (2011))
Dense
Not robust
High order information
Fast to calculate
Sparse
CV lab. seminar

4. Key idea
Propose objectness score that counts the number of contours wholly enclosed by a bounding box
CV lab. seminar

5. Model outline Extract edge map Edge set clustering
Find object proposals
CV lab. seminar

6. Structured edge prediction[ICCV13Dollar]
[ICCV13Dollar] Dollár, Piotr, and C. Lawrence Zitnick. "Structured forests for fast edge detection." Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE, 2013.
CV lab. seminar

7. Edge groups and affinities
Clustering edge into edge groups
Affinity between two edge groups
𝑎 𝑠 𝑖, , 𝑠 𝑗 =0
if two groups are separated by more than two pixels. Otherwise,
Markov assumption
직전 time slice의 subsequence와 segmentation result만이 영향을 미친다.
이 식의 계산에 dynamic programming을 사용할 수 없다.
Hierarchical video segmentation에서는 보통 explicit energy function이 없고,
S_i의 space가 너무 크기 때문이다.
Strong Markov approximation
각 시점의 segmentation result는 과거의 segmentation에 independent하다.
CV lab. seminar

8. Bounding box scoring Enclosedness coefficient
𝑤 𝑏 𝑠 𝑖 =0 𝑖𝑓 𝑠 𝑖 𝑖𝑠 𝑜𝑢𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑜𝑥
𝑤 𝑏 𝑠 𝑖 =0 𝑖𝑓 𝑠 𝑖 𝑖𝑠 𝑜𝑛 𝑡ℎ𝑒 𝑏𝑜𝑢𝑛𝑑𝑎𝑟𝑦
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒,
CV lab. seminar

9. Bounding box scoring Bounding box scoring
Reducing effects of edges near the center
Using an integral image to speed computation
CV lab. seminar

10. Finding intersecting edge groups
Data structures for finding 𝑆 𝑏 efficiently
𝐿 𝑟 =[0, 4, 0, 2, 0, 3, 0, 7, 1, 0, 1, 0]
𝐾 𝑟 =[1, 1, 1, 2, 2, 3, 3,4, 4, 5, 5, …10, 11, 11, 12, 12]
𝑠 4
𝑠 2
𝑠 3
𝑠 7
𝑠 1
𝑦=𝑟
CV lab. seminar

11. Search strategy Explicit control over diversity versus accuracy
Desired Intersection over Union (IoU): 𝛿
Initial step size: 𝛼 (𝛿∝𝛼)
Scale (==box area): 𝜎 ~ 𝑓𝑢𝑙𝑙 𝑖𝑚𝑎𝑔𝑒 (𝜎=1000𝑝𝑥)
Aspect ratio: 1/𝜏 ~ 𝜏 (𝜏=3)
Calculate score -> ½ step size -> Thresholding -> Calculate score -> ½ step size -> Thresholding -> …
Non-maximal suppression
CV lab. seminar

12. Benchmark[arXiv2014Hosang]
Quality over Pascal VOC 2007 validation set
Recall at IoU above 0.5 versus # of proposed windows
Recall versus IoU threshold (for 1000 proposals per image)
Bing
CPMC
EdgeBoxes
Endres
MCG
Objectness
Rahtu
Rand.Prim
Ranta.2014
Sel.Search
Gaussian
Sliding window
Superpixels
Uniform
area under the curve
(avg # of windows per image)
[arXiv2014Hosang] Hosang, Jan, Rodrigo Benenson, and Bernt Schiele. "How good are detection proposals, really?." arXiv preprint arXiv: (2014).
CV lab. seminar

13. Benchmark[arXiv2014Hosang]
Quality over ImageNet 2013 validation set
Recall at IoU above 0.5 versus # of proposed windows
Recall versus IoU threshold (for 1000 proposals per image)
Bing
EdgeBoxes
Endres
MCG
Rand.Prim
Sel.Search
Gaussian
Sliding window
Superpixels
[arXiv2014Hosang] Hosang, Jan, Rodrigo Benenson, and Bernt Schiele. "How good are detection proposals, really?." arXiv preprint arXiv: (2014).
CV lab. seminar

14. Comparison table[arXiv2014Hosang]
CV lab. seminar

15. Thank you!
CV lab. seminar