671 research outputs found
Multiscale combinatorial grouping for image segmentation and object proposal generation
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five seconds per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.Peer ReviewedPostprint (author's final draft
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
Improving Spatial Codification in Semantic Segmentation
This paper explores novel approaches for improving the spatial codification
for the pooling of local descriptors to solve the semantic segmentation
problem. We propose to partition the image into three regions for each object
to be described: Figure, Border and Ground. This partition aims at minimizing
the influence of the image context on the object description and vice versa by
introducing an intermediate zone around the object contour. Furthermore, we
also propose a richer visual descriptor of the object by applying a Spatial
Pyramid over the Figure region. Two novel Spatial Pyramid configurations are
explored: Cartesian-based and crown-based Spatial Pyramids. We test these
approaches with state-of-the-art techniques and show that they improve the
Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic
segmentation challenges.Comment: Paper accepted at the IEEE International Conference on Image
Processing, ICIP 2015. Quebec City, 27-30 September. Project page:
https://imatge.upc.edu/web/publications/improving-spatial-codification-semantic-segmentatio
Multiscale combinatorial grouping
We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates. 1
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