Unsupervised Detection of Regions of Interest Using Iterative Link Analysis

NeurIPS 2009 Gunhee KimAntonio Torralba

This paper proposes a fast and scalable alternating optimization technique to detect regions of interest (ROIs) in cluttered Web images without labels. The proposed approach discovers highly probable regions of object instances by iteratively repeating the following two functions: (1) choose the exemplar set (i.e. small number of high ranked reference ROIs) across the dataset and (2) refine the ROIs of each image with respect to the exemplar set... (read more)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet