Weakly supervised object localization via maximal entropy random walk

In this paper, we investigate the problem of weakly supervised object localization in images. For such a problem, the goal is to predict the locations of objects in test images while the labels of the training images are given at image-level. That means a label only indicates whether an image contains objects or not, but does not provide the exact locations of the objects. We propose to address this problem using Maximal Entropy Random Walk (MERW). Specifically, we first train a linear SVM classifier with the weakly labeled data. Based on bag-of-words feature representation, the response of a region to the linear SVM classifier can be formulated as the sum of the feature-weights within the region. For a test image, by properly constructing a graph on the feature-points, the stationary distribution of a MERW can indicate the region with the densest positive feature-weights, and thus provides a probabilistic object localization. Experiments compared with state-of-the-art methods on two datasets validate the performance of our method.