Edge boxes: Locating object proposals from edges 2014. 9. 23. Mooyeol Baek Zitnick, C. Lawrence, and Piotr Dollár. "Edge boxes: Locating object proposals from edges." Computer Vision–ECCV 2014. Springer International Publishing, 2014. 391-405.

Object proposals Finds general candidates of object efficiently 2014-09-23 CV lab. seminar

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 2014-09-23 CV lab. seminar

Key idea Propose objectness score that counts the number of contours wholly enclosed by a bounding box 2014-09-23 CV lab. seminar

Model outline Extract edge map Edge set clustering Find object proposals 2014-09-23 CV lab. seminar

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. 2014-09-23 CV lab. seminar

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. 2014-09-23 CV lab. seminar

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 2014-09-23

Bounding box scoring Bounding box scoring Reducing effects of edges near the center Using an integral image to speed computation 2014-09-23 CV lab. seminar

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 2014-09-23 CV lab. seminar

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 2014-09-23 CV lab. seminar

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:1406.6962 (2014). 2014-09-23 CV lab. seminar

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:1406.6962 (2014). 2014-09-23 CV lab. seminar

Comparison table[arXiv2014Hosang] 2014-09-23 CV lab. seminar

Thank you! 2014-09-23 CV lab. seminar