title: Using hidden scale for salient object detection.
abstract: This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.
authors.affiliation: Centre de Mathématiques et de Leurs Applications, CNRS (UMR 8536), Ecole Normale Supérieure de Cachan, 94235 Cachan, France. bernard.chalmond@cmla.ens-cachan.fr
authors.forename: Bernard
authors.initials: B
authors.lastname: Chalmond
authors.forename: Benjamin
authors.initials: B
authors.lastname: Francesconi
authors.forename: Stéphane
authors.initials: S
authors.lastname: Herbin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*