Object Recognition by Integrating Multiple Image Segmentations

The joint tasks of object recognition and object segmentation from a single image are complex in their requirement of not only correct classification, but also deciding exactly which pixels belong to the object. Exploring all possible pixel subsets is prohibitively expensive, leading to recent approaches which use unsupervised image segmentation to reduce the size of the configuration space. Image segmentation, however, is known to be unstable, strongly affected by small image perturbations, feature choices, or different segmentation algorithms. This instability has led to advocacy for using multiple segmentations of an image. In this paper, we explore the question of how to best integrate the information from multiple bottom-up segmentations of an image to improve object recognition robustness. By integrating the image partition hypotheses in an intuitive combined top-down and bottom-up recognition approach, we improve object and feature support. We further explore possible extensions...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Caroline Pantofaru, Cordelia Schmid, Martial Hebert
• Keywords: Computer Vision, ECCV 2008, Image Partition Hypotheses, Multiple Bottom-up Segmentations, Object Recognition Robustness, Object Segmentation Challenge, Unsupervised Image Segmentation