Bottom-up Object Segmentation for Visual Recognition

Abstract

Automatic recognition and segmentation of objects in images is a central open problem in computer vision. Most previous approaches have pursued either sliding-window object detection or dense classification of overlapping local image patches. Differently, the framework introduced in this thesis attempts to identify the spatial extent of objects prior to recognition, using bottom-up computational processes and mid-level selection cues. After a set of plausible object hypotheses is identified, a sequential recognition process is executed, based on continuous estimates of the spatial overlap between the image segment hypotheses and each putative class. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge of the properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. It is show that CPMC significantly outperforms the state of the art for low-level segmentation in the PASCAL VOC 2009 and 2010 datasets. Results beyond the current state of the art for image classification, object detection and semantic segmentation are also demonstrated in a number of challenging datasets including Caltech-101, ETHZ-Shape as well as PASCAL VOC 2009-11. These results suggest that a greater emphasis on grouping and image organization may be valuable for making progress in high-level tasks such as object recognition and scene understanding