Semantic contours from inverse detectors
Top Cited Papers
- 1 November 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method for combining generic object detectors with bottom-up contours to identify object contours. We also provide a principled way of combining information from different part detectors and across categories. In order to study the problem and evaluate quantitatively our approach, we present a dataset of semantic exterior boundaries on more than 20, 000 object instances belonging to 20 categories, using the images from the VOC2011 PASCAL challenge [7].Keywords
This publication has 14 references indexed in Scilit:
- Recovering Occlusion Boundaries from an ImageInternational Journal of Computer Vision, 2010
- Contour Detection and Hierarchical Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- Highly accurate boundary detection and groupingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Object Detection Combining Recognition and SegmentationPublished by Springer Science and Business Media LLC ,2007
- LabelMe: A Database and Web-Based Tool for Image AnnotationInternational Journal of Computer Vision, 2007
- Untangling Cycles for Contour GroupingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Learning to Find Object Boundaries Using Motion CuesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Classifier-based Contour Tracking for Rigid and Deformable ObjectsPublished by British Machine Vision Association and Society for Pattern Recognition ,2005
- Learning to detect natural image boundaries using local brightness, color, and texture cuesIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statisticsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002