5,967 research outputs found
Design and perceptual validation of performance measures for salient object segmentation
Empirical evaluation of salient object segmentation methods requires i) a dataset of ground truth object segmen-tations and ii) a performance measure to compare the out-put of the algorithm with the ground truth. In this paper, we provide such a dataset, and evaluate 5 distinct performance measures that have been used in the literature practically and psychophysically. Our results suggest that a measure based upon minimal contour mappings is most sensitive to shape irregularities and most consistent with human judge-ments. In fact, the contour mapping measure is as predic-tive of human judgements as human subjects are of each other. Region-based methods, and contour methods such as Hausdorff distances that do not respect the ordering of points on shape boundaries are significantly less consistent with human judgements. We also show that minimal contour mappings can be used as the correspondence paradigm for Precision-Recall analysis. Our findings can provide guid-ance in evaluating the results of segmentation algorithms in the future. 1
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of parameters. In this work, we question the need to have such
memory demanding networks for the specific task of salient object segmentation.
To this end, we propose a way to learn a memory-efficient network from scratch
by training it only on salient object detection datasets. Our method encodes
images to gridized superpixels that preserve both the object boundaries and the
connectivity rules of regular pixels. This representation allows us to use
convolutional neural networks that operate on regular grids. By using these
encoded images, we train a memory-efficient network using only 0.048\% of the
number of parameters that other deep salient object detection networks have.
Our method shows comparable accuracy with the state-of-the-art deep salient
object detection methods and provides a faster and a much more memory-efficient
alternative to them. Due to its easy deployment, such a network is preferable
for applications in memory limited devices such as mobile phones and IoT
devices.Comment: 6 pages, submitted to MMSP 201
How is Gaze Influenced by Image Transformations? Dataset and Model
Data size is the bottleneck for developing deep saliency models, because
collecting eye-movement data is very time consuming and expensive. Most of
current studies on human attention and saliency modeling have used high quality
stereotype stimuli. In real world, however, captured images undergo various
types of transformations. Can we use these transformations to augment existing
saliency datasets? Here, we first create a novel saliency dataset including
fixations of 10 observers over 1900 images degraded by 19 types of
transformations. Second, by analyzing eye movements, we find that observers
look at different locations over transformed versus original images. Third, we
utilize the new data over transformed images, called data augmentation
transformation (DAT), to train deep saliency models. We find that label
preserving DATs with negligible impact on human gaze boost saliency prediction,
whereas some other DATs that severely impact human gaze degrade the
performance. These label preserving valid augmentation transformations provide
a solution to enlarge existing saliency datasets. Finally, we introduce a novel
saliency model based on generative adversarial network (dubbed GazeGAN). A
modified UNet is proposed as the generator of the GazeGAN, which combines
classic skip connections with a novel center-surround connection (CSC), in
order to leverage multi level features. We also propose a histogram loss based
on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in
terms of luminance distribution. Extensive experiments and comparisons over 3
datasets indicate that GazeGAN achieves the best performance in terms of
popular saliency evaluation metrics, and is more robust to various
perturbations. Our code and data are available at:
https://github.com/CZHQuality/Sal-CFS-GAN
Deep Contrast Learning for Salient Object Detection
Salient object detection has recently witnessed substantial progress due to
powerful features extracted using deep convolutional neural networks (CNNs).
However, existing CNN-based methods operate at the patch level instead of the
pixel level. Resulting saliency maps are typically blurry, especially near the
boundary of salient objects. Furthermore, image patches are treated as
independent samples even when they are overlapping, giving rise to significant
redundancy in computation and storage. In this CVPR 2016 paper, we propose an
end-to-end deep contrast network to overcome the aforementioned limitations.
Our deep network consists of two complementary components, a pixel-level fully
convolutional stream and a segment-wise spatial pooling stream. The first
stream directly produces a saliency map with pixel-level accuracy from an input
image. The second stream extracts segment-wise features very efficiently, and
better models saliency discontinuities along object boundaries. Finally, a
fully connected CRF model can be optionally incorporated to improve spatial
coherence and contour localization in the fused result from these two streams.
Experimental results demonstrate that our deep model significantly improves the
state of the art.Comment: To appear in CVPR 201
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