Selective Search is a region proposal algorithm for object detection tasks. It starts by over-segmenting the image based on intensity of the pixels using a graph-based segmentation method by Felzenszwalb and Huttenlocher. Selective Search then takes these oversegments as initial input and performs the following steps
At each iteration, larger segments are formed and added to the list of region proposals. Hence we create region proposals from smaller segments to larger segments in a bottom-up approach. This is what we mean by computing “hierarchical” segmentations using Felzenszwalb and Huttenlocher’s oversegments.
|Few-Shot Object Detection||2||5.00%|
|Reinforcement Learning (RL)||1||2.50%|
|Medical Image Segmentation||1||2.50%|
|🤖 No Components Found||You can add them if they exist; e.g. Mask R-CNN uses RoIAlign|