8 research outputs found
Sequential Optimization for Efficient High-Quality Object Proposal Generation
We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BING but significantly improves its proposal localization
quality. At high level we formulate the problem of object proposal generation
from a novel probabilistic perspective, based on which our BING++ manages to
improve the localization quality by employing edges and segments to estimate
object boundaries and update the proposals sequentially. We propose learning
the parameters efficiently by searching for approximate solutions in a
quantized parameter space for complexity reduction. We demonstrate the
generalization of BING++ with the same fixed parameters across different object
classes and datasets. Empirically our BING++ can run at half speed of BING on
CPU, but significantly improve the localization quality by 18.5% and 16.7% on
both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other
state-of-the-art approaches, BING++ can achieve comparable performance, but run
significantly faster.Comment: Accepted by TPAM
Sequential optimization for efficient high-quality object proposal generation
We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster
Hierarchical improvement of foreground segmentation masks in background subtraction
A plethora of algorithms have been defined for foreground
segmentation, a fundamental stage for many computer
vision applications. In this work, we propose a post-processing
framework to improve foreground segmentation performance of
background subtraction algorithms. We define a hierarchical
framework for extending segmented foreground pixels to undetected
foreground object areas and for removing erroneously
segmented foreground. Firstly, we create a motion-aware hierarchical
image segmentation of each frame that prevents merging
foreground and background image regions. Then, we estimate
the quality of the foreground mask through the fitness of the
binary regions in the mask and the hierarchy of segmented
regions. Finally, the improved foreground mask is obtained as
an optimal labeling by jointly exploiting foreground quality and
spatial color relations in a pixel-wise fully-connected Conditional
Random Field. Experiments are conducted over four large and
heterogeneous datasets with varied challenges (CDNET2014,
LASIESTA, SABS and BMC) demonstrating the capability of the
proposed framework to improve background subtraction resultsThis work was partially supported by the Spanish Government
(HAVideo, TEC2014-53176-R