@mastersthesis {xBellver-Bueno, title = {Efficient Exploration of Region Hierarchies for Semantic Segmentation}, year = {2015}, abstract = {

Advisors: Carles Ventura-Royo (UPC) and Xavier Gir{\'o}-i-Nieto (UPC)

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9.0/10.0)

The motivation of this work is the efficient exploration of hierarchical partitions for semantic segmentation as a method for locating objects in images. While many efforts have been focused on efficient image search in large-scale databases, few works have addressed the problem of locating and recognizing objects efficiently within a given image. My work considers as an input a hierarchical partition of an image that defines a set of regions as candidate locations to contain an object. This approach will be compared to other state of the art algorithms that extract object candidates for an image. The final goal of this work is to semantically segment images efficiently by exploiting the multiscale information provided by a hierarchical partition, maximizing the accuracy of the segmentation when only a very few regions of the partition are analysed.

Efficient exploration of region hierarchies for semantic segmentation from Xavier Giro

}, author = {M{\'\i}riam Bellver}, editor = {Ventura, C. and Xavier Gir{\'o}-i-Nieto} }