1,687 research outputs found
Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools
during region agglomeration. We propose an active learning approach for
performing hierarchical agglomerative segmentation from superpixels. Our method
combines multiple features at all scales of the agglomerative process, works
for data with an arbitrary number of dimensions, and scales to very large
datasets. We advocate the use of variation of information to measure
segmentation accuracy, particularly in 3D electron microscopy (EM) images of
neural tissue, and using this metric demonstrate an improvement over competing
algorithms in EM and natural images.Comment: 15 pages, 8 figure
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
Superpixels: An Evaluation of the State-of-the-Art
Superpixels group perceptually similar pixels to create visually meaningful
entities while heavily reducing the number of primitives for subsequent
processing steps. As of these properties, superpixel algorithms have received
much attention since their naming in 2003. By today, publicly available
superpixel algorithms have turned into standard tools in low-level vision. As
such, and due to their quick adoption in a wide range of applications,
appropriate benchmarks are crucial for algorithm selection and comparison.
Until now, the rapidly growing number of algorithms as well as varying
experimental setups hindered the development of a unifying benchmark. We
present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms
utilizing a benchmark focussing on fair comparison and designed to provide new
insights relevant for applications. To this end, we explicitly discuss
parameter optimization and the importance of strictly enforcing connectivity.
Furthermore, by extending well-known metrics, we are able to summarize
algorithm performance independent of the number of generated superpixels,
thereby overcoming a major limitation of available benchmarks. Furthermore, we
discuss runtime, robustness against noise, blur and affine transformations,
implementation details as well as aspects of visual quality. Finally, we
present an overall ranking of superpixel algorithms which redefines the
state-of-the-art and enables researchers to easily select appropriate
algorithms and the corresponding implementations which themselves are made
publicly available as part of our benchmark at
davidstutz.de/projects/superpixel-benchmark/
CRF Learning with CNN Features for Image Segmentation
Conditional Random Rields (CRF) have been widely applied in image
segmentations. While most studies rely on hand-crafted features, we here
propose to exploit a pre-trained large convolutional neural network (CNN) to
generate deep features for CRF learning. The deep CNN is trained on the
ImageNet dataset and transferred to image segmentations here for constructing
potentials of superpixels. Then the CRF parameters are learnt using a
structured support vector machine (SSVM). To fully exploit context information
in inference, we construct spatially related co-occurrence pairwise potentials
and incorporate them into the energy function. This prefers labelling of object
pairs that frequently co-occur in a certain spatial layout and at the same time
avoids implausible labellings during the inference. Extensive experiments on
binary and multi-class segmentation benchmarks demonstrate the promise of the
proposed method. We thus provide new baselines for the segmentation performance
on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC
2011 datasets
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