Weakly Supervised Top-down Image Segmentation

Authors:
Manuela Vasconcelos;Nuno Vasconcelos;Gustavo Carneiro
Affiliations:
University of California, San Diego;University of California, San Diego;Siemens Corporate Research
Venue:
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Year:
2006

Citing 0
Cited 4

Leveraging social media for training object detectors

DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Combining image-level and object-level inference for weakly supervised object recognition. application to fisheries acoustics

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Object recognition using proportion-based prior information: Application to fisheries acoustics

Pattern Recognition Letters
A method for noise-robust context-aware pattern discovery and recognition from categorical sequences

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

There has recently been significant interest in top-down image segmentation methods, which incorporate the recognition of visual concepts as an intermediate step of segmentation. This work addresses the problem of top-down segmentation with weak supervision. Under this framework, learning does not require a set of manually segmented examples for each concept of interest, but simply a weakly labeled training set. This is a training set where images are annotated with a set of keywords describing their contents, but visual concepts are not explicitly segmented and no correspondence is specified between keywords and image regions. We demonstrate, both analytically and empirically, that weakly supervised segmentation is feasible when certain conditions hold. We also propose a simple weakly supervised segmentation algorithm that extends state-of-theart bottom-up segmentation methods in the direction of perceptually meaningful segmentation1.