10,046 research outputs found
Salient Object Detection via Augmented Hypotheses
In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Structured-output learning is a challenging problem; particularly so because
of the difficulty in obtaining large datasets of fully labelled instances for
training. In this paper we try to overcome this difficulty by presenting a
multi-utility learning framework for structured prediction that can learn from
training instances with different forms of supervision. We propose a unified
technique for inferring the loss functions most suitable for quantifying the
consistency of solutions with the given weak annotation. We demonstrate the
effectiveness of our framework on the challenging semantic image segmentation
problem for which a wide variety of annotations can be used. For instance, the
popular training datasets for semantic segmentation are composed of images with
hard-to-generate full pixel labellings, as well as images with easy-to-obtain
weak annotations, such as bounding boxes around objects, or image-level labels
that specify which object categories are present in an image. Experimental
evaluation shows that the use of annotation-specific loss functions
dramatically improves segmentation accuracy compared to the baseline system
where only one type of weak annotation is used
Adaptive Nonparametric Image Parsing
In this paper, we present an adaptive nonparametric solution to the image
parsing task, namely annotating each image pixel with its corresponding
category label. For a given test image, first, a locality-aware retrieval set
is extracted from the training data based on super-pixel matching similarities,
which are augmented with feature extraction for better differentiation of local
super-pixels. Then, the category of each super-pixel is initialized by the
majority vote of the -nearest-neighbor super-pixels in the retrieval set.
Instead of fixing as in traditional non-parametric approaches, here we
propose a novel adaptive nonparametric approach which determines the
sample-specific k for each test image. In particular, is adaptively set to
be the number of the fewest nearest super-pixels which the images in the
retrieval set can use to get the best category prediction. Finally, the initial
super-pixel labels are further refined by contextual smoothing. Extensive
experiments on challenging datasets demonstrate the superiority of the new
solution over other state-of-the-art nonparametric solutions.Comment: 11 page
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