Object Localization on Natural Scenes: A Survey

Abstract

Object localization is one of the inherent tasks of computer vision. It plays an intrinsic role in object detection tasks that initiate with a recognition procedure of figuring out the presence of single/multiple instances of objects of interest in a given image. It involves determination of precise locations of object instances. This paper presents an overview of some of the popularly used approaches to the object localization problem, involving efficient branch-and-bound strategy for sub-window search, super-pixel neighborhood information based approach, boosted local structured Histogram of Oriented Gradients-Local Binary Patterns (HOG-LBP) based strategy, multi-instance learning based weakly supervised object localization, object localization by utilizing deep networks and image tag based object localization. The performance of the mentioned approaches have been compared on the basis of their results on PASCAL-VOC 2007 dataset.