9,816 research outputs found
Fully automatic extraction of salient objects from videos in near real-time
Automatic video segmentation plays an important role in a wide range of
computer vision and image processing applications. Recently, various methods
have been proposed for this purpose. The problem is that most of these methods
are far from real-time processing even for low-resolution videos due to the
complex procedures. To this end, we propose a new and quite fast method for
automatic video segmentation with the help of 1) efficient optimization of
Markov random fields with polynomial time of number of pixels by introducing
graph cuts, 2) automatic, computationally efficient but stable derivation of
segmentation priors using visual saliency and sequential update mechanism, and
3) an implementation strategy in the principle of stream processing with
graphics processor units (GPUs). Test results indicates that our method
extracts appropriate regions from videos as precisely as and much faster than
previous semi-automatic methods even though any supervisions have not been
incorporated.Comment: submitted to Special Issue on High Performance Computation on
Hardware Accelerators, the Computer Journa
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Salient object detection has been attracting a lot of interest, and recently
various heuristic computational models have been designed. In this paper, we
formulate saliency map computation as a regression problem. Our method, which
is based on multi-level image segmentation, utilizes the supervised learning
approach to map the regional feature vector to a saliency score. Saliency
scores across multiple levels are finally fused to produce the saliency map.
The contributions lie in two-fold. One is that we propose a discriminate
regional feature integration approach for salient object detection. Compared
with existing heuristic models, our proposed method is able to automatically
integrate high-dimensional regional saliency features and choose discriminative
ones. The other is that by investigating standard generic region properties as
well as two widely studied concepts for salient object detection, i.e.,
regional contrast and backgroundness, our approach significantly outperforms
state-of-the-art methods on six benchmark datasets. Meanwhile, we demonstrate
that our method runs as fast as most existing algorithms
Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model
Almost all previous works on saliency detection have been dedicated to
conventional images, however, with the outbreak of panoramic images due to the
rapid development of VR or AR technology, it is becoming more challenging,
meanwhile valuable for extracting salient contents in panoramic images.
In this paper, we propose a novel bottom-up salient object detection
framework for panoramic images. First, we employ a spatial density estimation
method to roughly extract object proposal regions, with the help of region
growing algorithm. Meanwhile, an eye fixation model is utilized to predict
visually attractive parts in the image from the perspective of the human visual
search mechanism. Then, the previous results are combined by the maxima
normalization to get the coarse saliency map. Finally, a refinement step based
on geodesic distance is utilized for post-processing to derive the final
saliency map.
To fairly evaluate the performance of the proposed approach, we propose a
high-quality dataset of panoramic images (SalPan). Extensive evaluations
demonstrate the effectiveness of our proposed method on panoramic images and
the superiority of the proposed method against other methods.Comment: Previous Project website: https://github.com/ChunbiaoZhu/DCC-201
A Classifier-guided Approach for Top-down Salient Object Detection
We propose a framework for top-down salient object detection that
incorporates a tightly coupled image classification module. The classifier is
trained on novel category-aware sparse codes computed on object dictionaries
used for saliency modeling. A misclassification indicates that the
corresponding saliency model is inaccurate. Hence, the classifier selects
images for which the saliency models need to be updated. The category-aware
sparse coding produces better image classification accuracy as compared to
conventional sparse coding with a reduced computational complexity. A
saliency-weighted max-pooling is proposed to improve image classification,
which is further used to refine the saliency maps. Experimental results on
Graz-02 and PASCAL VOC-07 datasets demonstrate the effectiveness of salient
object detection. Although the role of the classifier is to support salient
object detection, we evaluate its performance in image classification and also
illustrate the utility of thresholded saliency maps for image segmentation.Comment: To appear in Signal Processing: Image Communication, Elsevier.
Available online from April 201
Computational models of attention
This chapter reviews recent computational models of visual attention. We
begin with models for the bottom-up or stimulus-driven guidance of attention to
salient visual items, which we examine in seven different broad categories. We
then examine more complex models which address the top-down or goal-oriented
guidance of attention towards items that are more relevant to the task at hand
SalProp: Salient object proposals via aggregated edge cues
In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. A Conditional Random Field is then learned to effectively
combine these features for edge classification with object/non-object label. We
propose an objectness score for the generated windows by analyzing the salient
edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007
dataset demonstrate that the proposed method gives competitive performance
against 10 popular generic object detection techniques while using fewer number
of proposals.Comment: 5 pages, 4 figures, accepted at ICIP 201
Weakly Supervised Learning for Salient Object Detection
Recent advances in supervised salient object detection has resulted in
significant performance on benchmark datasets. Training such models, however,
requires expensive pixel-wise annotations of salient objects. Moreover, many
existing salient object detection models assume that at least one salient
object exists in the input image. Such an assumption often leads to less
appealing saliency maps on the background images, which contain no salient
object at all. To avoid the requirement of expensive pixel-wise salient region
annotations, in this paper, we study weakly supervised learning approaches for
salient object detection. Given a set of background images and salient object
images, we propose a solution toward jointly addressing the salient object
existence and detection tasks. We adopt the latent SVM framework and formulate
the two problems together in a single integrated objective function: saliency
labels of superpixels are modeled as hidden variables and involved in a
classification term conditioned to the salient object existence variable, which
in turn depends on both global image and regional saliency features and
saliency label assignment. Experimental results on benchmark datasets validate
the effectiveness of our proposed approach.Comment: technical repor
Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception
To solve its task, a robot needs to have the ability to interpret its
perceptions. In vision, this interpretation is particularly difficult and
relies on the understanding of the structure of the scene, at least to the
extent of its task and sensorimotor abilities. A robot with the ability to
build and adapt this interpretation process according to its own tasks and
capabilities would push away the limits of what robots can achieve in a non
controlled environment. A solution is to provide the robot with processes to
build such representations that are not specific to an environment or a
situation. A lot of works focus on objects segmentation, recognition and
manipulation. Defining an object solely on the basis of its visual appearance
is challenging given the wide range of possible objects and environments.
Therefore, current works make simplifying assumptions about the structure of a
scene. Such assumptions reduce the adaptivity of the object extraction process
to the environments in which the assumption holds. To limit such assumptions,
we introduce an exploration method aimed at identifying moveable elements in a
scene without considering the concept of object. By using the interactive
perception framework, we aim at bootstrapping the acquisition process of a
representation of the environment with a minimum of context specific
assumptions. The robotic system builds a perceptual map called relevance map
which indicates the moveable parts of the current scene. A classifier is
trained online to predict the category of each region (moveable or
non-moveable). It is also used to select a region with which to interact, with
the goal of minimizing the uncertainty of the classification. A specific
classifier is introduced to fit these needs: the collaborative mixture models
classifier. The method is tested on a set of scenarios of increasing
complexity, using both simulations and a PR2 robot.Comment: 21 pages, 21 figure
Region-Based Multiscale Spatiotemporal Saliency for Video
Detecting salient objects from a video requires exploiting both spatial and
temporal knowledge included in the video. We propose a novel region-based
multiscale spatiotemporal saliency detection method for videos, where static
features and dynamic features computed from the low and middle levels are
combined together. Our method utilizes such combined features spatially over
each frame and, at the same time, temporally across frames using consistency
between consecutive frames. Saliency cues in our method are analyzed through a
multiscale segmentation model, and fused across scale levels, yielding to
exploring regions efficiently. An adaptive temporal window using motion
information is also developed to combine saliency values of consecutive frames
in order to keep temporal consistency across frames. Performance evaluation on
several popular benchmark datasets validates that our method outperforms
existing state-of-the-arts
Hierarchical Saliency Detection on Extended CSSD
Complex structures commonly exist in natural images. When an image contains
small-scale high-contrast patterns either in the background or foreground,
saliency detection could be adversely affected, resulting erroneous and
non-uniform saliency assignment. The issue forms a fundamental challenge for
prior methods. We tackle it from a scale point of view and propose a
multi-layer approach to analyze saliency cues. Different from varying patch
sizes or downsizing images, we measure region-based scales. The final saliency
values are inferred optimally combining all the saliency cues in different
scales using hierarchical inference. Through our inference model, single-scale
information is selected to obtain a saliency map. Our method improves detection
quality on many images that cannot be handled well traditionally. We also
construct an extended Complex Scene Saliency Dataset (ECSSD) to include complex
but general natural images.Comment: 14 pages, 15 figure
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